# Quantum Computing

*First published Sun Dec 3, 2006; substantive revision Tue Jun 16, 2015*

Combining physics, mathematics and computer science, quantum
computing has developed in the past two decades from a visionary idea
to one of the most fascinating areas of quantum mechanics. The recent
excitement in this lively and speculative domain of research was
triggered by Peter Shor (1994) who showed how a quantum algorithm
could exponentially “speed-up” classical computation and factor large
numbers into primes much more rapidly (at least in terms of the number
of computational steps involved) than any *known* classical
algorithm. Shor’s algorithm was soon followed by several other
algorithms that aimed to solve combinatorial and algebraic problems,
and in the last few years theoretical study of quantum systems serving
as computational devices has achieved tremendous progress. Common
belief has it that the implementation of Shor’s algorithm on a large
scale quantum computer would have devastating consequences for current
cryptography protocols which rely on the premise that all known
classical worst-case algorithms for *factoring* take time
exponential in the length of their input (see, e.g., Preskill
2005). Consequently, experimentalists around the world are engaged in
tremendous attempts to tackle the technological difficulties that
await the realization of such a large scale quantum computer. But
regardless whether these technological problems can be overcome (Unruh
1995, Ekert and Jozsa 1996, Haroche and Raimond 1996), it is
noteworthy that no proof exists yet for the *general*
superiority of quantum computers over their classical counterparts.

The philosophical interest in quantum computing is threefold: First,
from a social-historical perspective, quantum computing is a domain
where experimentalists find themselves ahead of their fellow
theorists. Indeed, quantum mysteries such as
entanglement
and
nonlocality
were historically considered a philosophical quibble, until
physicists discovered that these mysteries might be harnessed to
devise new efficient algorithms. But while the technology for
isolating 5 or even 7 qubits (the basic unit of information in the
quantum computer) is now within reach (Schrader *et al.* 2004,
Knill *et al.* 2000), only a handful of quantum algorithms
exist, and the question whether these can solve classically
intractable computational problems is still open. Next, from a more
philosophical perspective, advances in quantum computing may yield
foundational benefits. It may turn out that the technological
capabilities that allow us to isolate quantum systems by shielding
them from the effects of
decoherence
for a period of time long enough to manipulate them will also allow
us to make progress in some fundamental problems in the foundations of
quantum theory itself. Indeed, the development and the implementation
of efficient quantum algorithms may help us understand better the
border between classical and quantum physics, hence elucidate an
important problem, namely, the measurement problem, that so far resists a
solution. Finally, the idea that abstract mathematical concepts such
as *complexity* and *(in)tractability* may not only be
translated into physics, but also *re-written* by physics bears
directly on the autonomous character of computer science and the
status of its theoretical entities—the so-called “computational
kinds”. As such it is also relevant to the long-standing philosophical
debate on the relationship between mathematics and the physical
world.

- 1. A Brief History of the Field
- 2. Basics
- 3. Quantum Algorithms
- 4. Philosophical Implications
- Bibliography
- Academic Tools
- Other Internet Resources
- Related Entries

## 1. A Brief History of the Field

### 1.1 Physical Computational Complexity

The mathematical model for a “universal” computer was defined long before the invention of computers and is called the Turing machine (Turing 1936). A Turing machine consists of an unbounded tape, a head that is capable of reading from the tape and of writing onto it and can occupy an infinite number of possible states, and an instruction table (a transition function). This table, given the head’s initial state and the input it reads from the tape in that state, determines (a) the symbol that the head will write on the tape, (b) the internal state it will occupy, and (c) the displacement of the head on the tape. In 1936 Turing showed that since one can encode the instruction table of a Turing machine \(T\) and express it as a binary number \(\#(T)\), there exists a universal Turing machine \(U\) that can simulate the instruction table of any Turing machine on any given input with at most a polynomial slowdown (i.e., the number of computational steps required by \(U\) to execute the original program \(T\) on the original input will be polynomially bounded in \(\#(T)\)). That the Turing machine model (what we nowadays call “an algorithm”) captures the concept of computability in its entirety is the essence of the Church-Turing thesis, according to which any effectively calculable function can be computed using a Turing machine. Admittedly, no counterexample to this thesis (which is the result of convergent ideas of Turing, Post, Kleene and Church) has yet been found. But since it identifies the class of computable functions with the class of those functions which are computable using a Turing machine, this thesis involves both a precise mathematical notion and an informal and intuitive notion, hence cannot be proved or disproved. Simple cardinality considerations show, however, that not all functions are Turing-computable (the set of all Turing machines is countable, while the set of all functions from the natural numbers to the natural numbers is not), and the discovery of this fact came as a complete surprise in the 1930s (Davis 1958).

Computability, or the question whether a function can be computed, is
not the only question that interests computer scientists. The
*cost* of computing a function is also of great importance, and
this cost, also known as
computational complexity,
is measured naturally in the physical resources (e.g., time, space,
energy) invested in order to solve the computational problem at
hand. Computer scientists classify computational problems according to
the way their cost function behaves as a function of their input size,
\(n\) (the number of bits required to store the input) and in
particular, whether it increases exponentially or polynomially with
\(n\). *Tractable* problems are those which can be solved
in polynomial cost, while *intractable* problems are those
which can only be solved in an exponential cost (the former solutions
are commonly regarded as *efficient* although an
exponential-time algorithm could turn out to be more efficient than a
polynomial-time algorithm for some range of input sizes). If we
further relax the requirement that a solution to a computational
problem be always correct, and allow probabilistic algorithms with a
negligible probability of error, we can dramatically reduce the
computational cost. Probabilistic algorithms are non-deterministic
Turing machines whose transition function can randomly change the
head’s configuration in one of several possible ways. The most famous
example of an algorithm of this kind is the probabilistic algorithm
that decides whether a given natural number is prime in a polynomial
number of steps (Rabin 1976).

Using this notion we can further refine
the distinction between tractable and intractable problems. The class
\(\mathbf{P}\) (for Polynomial) is the class that contains all the
computational decision problems that can be *solved* by a deterministic
Turing machine at a polynomial cost. The class **NP**
(for Non-deterministic Polynomial) is the class that contains all
those computational decision problems whose proposed solution (“guessed” by the
non-deterministic Turing machine) can be *verified* by a
deterministic Turing machine at polynomial cost. The most famous
problems in **NP** are called
“**NP**-complete”. The term “complete” designates the
fact that these problems stand or fall together: Either they are all
tractable, or none of them is! If we knew how to solve an
**NP**-complete problem efficiently (i.e., with a
polynomial cost) we could solve any problem in **NP**
with only polynomial slowdown (Cook 1971). Today we know of hundreds
of examples of **NP**-complete problems (Garey and
Johnson 1979), all of which are reducible one to another in polynomial
slowdown, and since the best known algorithm for any of these problems
is exponential, the widely believed conjecture is that there is no
polynomial algorithm that can solve them. But while clearly
\(\mathbf{P} \subseteq \mathbf{NP}\), proving or disproving
the conjecture that \(\mathbf{P} \ne \mathbf{NP}\)
remains perhaps one of the most important open questions in computer
science and complexity theory.

Although the original Church-Turing thesis involved the
*abstract* mathematical notion of computability, physicists as
well as computer scientists often interpret it as saying something
about the scope and limitations of *physical* computing
machines. Wolfram (1985) claims that any physical system
can be simulated (to any degree of approximation) by a universal
Turing machine, and that complexity bounds on Turing machine
simulations have physical significance. For example, if the
computation of the minimum energy of some system of \(n\)
particles requires at least an exponentially increasing number of
steps in \(n\), then the actual relaxation of this system to its
minimum energy state will also take an exponential time. Aharonov
(1998) strengthens this thesis (in the context of showing its putative
incompatibility with quantum mechanics) when she says that a
probabilistic Turing machine can simulate any reasonable physical
device at polynomial cost. Further examples for this thesis can be
found in Copeland (1996). In order for the physical Church-Turing
thesis to make sense we have to relate the space and time parameters
of physics to their computational counterparts: memory capacity and
number of computation steps, respectively. There are various ways to
do that, leading to different formulations of the thesis (Pitowsky
1990). For example, one can encode the set of instructions of a
universal Turing machine and the state of its infinite tape in the
binary development of the position coordinates of a single
particle. Consequently, one can physically ‘realize’ a
universal Turing machine as a billiard ball with hyperbolic mirrors
(Moore 1990, Pitowsky 1996). For the most intuitive connection between
abstract Turing machines and physical devices see the pioneering work
of Gandy (1980), simplified later by Sieg and Byrnes (1999). It should
be stressed that there is no relation between the original
Church-Turing thesis and its physical version (Pitowsky and Shagrir
2003), and while the former concerns the concept of computation that
is relevant to logic (since it is strongly tied to the notion of
*proof* which requires *validation*), it does not
analytically entail that *all* computations should be subject
to validation. Indeed, there is a long historical tradition of analog
computations which use continuous physical processes (Dewdney 1984),
and the output of these computations is validated either by repetitive
“runs” or by validating the physical theory that presumably governs
the behavior of the analog computer.

### 1.2 Physical “Short-cuts” of Computation

Do physical processes exist which contradict the physical
Church-Turing thesis? Apart from analog computation, there exist at
least two counter-examples to this thesis that purport to show that
the notion of recursion, or Turing-computability, is not a natural
*physical* property (Pour-el and Richards 1981, Pitowsky 1990,
Hogarth 1994). Although the physical systems involved (a specific
initial condition for the wave equation in three dimensions and an
exotic solution to Einstein’s field equations, respectively) are
somewhat contrived, recent years saw the emergence of the thriving
school of “hypercomputation” that aspires to extend the limited
examples of physical “hypercomputers” and in so doing to physically
“compute” the non-Turing-computable (for a review see Copeland 2002;
for a criticism—Davis 2003). *Quantum* hypercomputation is
rarely discussed in the literature (See, e.g., Calude *et al.*
2003), but the most concrete attempt to harness quantum theory to
compute the non-computable is the suggestion to use the quantum
adiabatic algorithm (see below) to solve Hilbert’s Tenth Problem (Kieu
2002, 2004)—a Turing-undecidable problem equivalent to the halting
problem. Recent criticism, however, has exposed the unphysical
character of the alleged quantum adiabatic hypercomputer (see Smith
2005, Hodges 2005, and Hagar and Korolev 2007).

Setting aside the hype around “hypercomputers”, even if we restrict
ourselves only to Turing-computable functions and focus on
computational complexity, we can still find many physical models that
purport to display “short-cuts” in computational resources. Consider,
e.g., the DNA model of computation that was claimed (Adleman 1994,
Lipton 1995) to solve **NP**-complete problems in
polynomial time. A closer inspection shows that the cost of the
computation in this model is still exponential since the number of
molecules in the physical system grows exponentially with the size of
the problem. Or take an allegedly instantaneous solution to another
**NP**-complete problem using a construction of rods and
balls (Vergis *et al.* 1986) that unfortunately ignores the
accumulating time-delays in the rigid rods that result in an
exponential overall slowdown. Another example is the physical
simulation of the factorization of numbers into primes that uses only
polynomial resources in *time* and *space*, but requires
an exponentially increasing *precision*. It thus appears that
all these models cannot serve as counter-examples to the physical
Church-Turing thesis (as far as complexity is concerned) since they
all require some exponential physical resource. Note, however, that
all these models are based on classical physics, hence the unavoidable
question: Can the shift to quantum physics allow us to find
“short-cuts” in computational resources? The quest for the quantum
computer began with the possibility of giving a positive answer to
this question.

### 1.3 Milestones

The idea of a computational device based on quantum mechanics was explored already in the 1970s by physicists and computer scientists. As early as 1969 Steven Wiesner suggested quantum information processing as a possible way to better accomplish cryptologic tasks. But the first four published papers on quantum information (Wiesner published his only in 1983), belong to Alexander Holevo (1973), R.P. Poplavskii (1975), Roman Ingarden (1976) and Yuri Manin (1980). Better known are contributions made in the early 1980s by Charles H. Bennett of the IBM Thomas J. Watson Research Center, Paul A. Benioff of Argonne National Laboratory in Illinois, David Deutsch of the University of Oxford, and the late Richard P. Feynman of the California Institute of Technology. The idea emerged when scientists were investigating the fundamental physical limits of computation. If technology continued to abide by “Moore’s Law” (the observation made in 1965 by Gordon Moore, co-founder of Intel, that the number of transistors per square inch on integrated circuits had doubled every 18 months since the integrated circuit was invented), then the continually shrinking size of circuitry packed onto silicon chips would eventually reach a point where individual elements would be no larger than a few atoms. But since the physical laws that govern the behavior and properties of the putative circuit at the atomic scale are inherently quantum mechanical in nature, not classical, the natural question arose whether a new kind of computer could be devised based on the principles of quantum physics.

Inspired by Ed Fredkin’s ideas on reversible computation (see Hagar forthcoming),
Feynman was among the first to attempt to provide an answer to this
question by producing an abstract model in 1982 that showed how a
quantum system could be used to do computations. He also explained
how such a machine would be able to act as a simulator for quantum
physics. Feynman also conjectured that any classical computer that
will be harnessed for this task will do so only inefficiently,
incurring an exponential slowdown in computation time. In 1985 David
Deutsch proposed the first universal quantum Turing machine and paved
the way to the quantum circuit model. The young and thriving domain
also attracted philosophers’ attention. In 1983 David Albert showed
how a quantum mechanical automaton behaves remarkably differently from
a classical automaton, and in 1990 Itamar Pitowsky raised the question
whether the superposition principle will allow quantum computers to
solve **NP**-complete problems. He also stressed that
although one could in principle ‘squeeze’ information of
exponential complexity into polynomially many quantum states, the real
problem lay in the efficient retrieval of this information.

Progress in quantum algorithms began in the 1990s, with the discovery
of the Deutsch-Josza oracle (1992) and of Simon’s oracle (1994). The
latter supplied the basis for Shor’s algorithm for
*factoring*. Published in 1994, this algorithm marked a
‘phase transition’ in the development of quantum computing
and sparked a tremendous interest even outside the physics
community. In that year the first experimental realization of the
quantum *CNOT* gate with trapped ions was proposed by Cirac and
Zoller (1995). In 1995, Peter Shor and Andrew Steane proposed
(independently) the first scheme for quantum error-correction. In that
same year the first realization of a quantum logic gate was done in
Boulder, Colorado, following Cirac and Zoller’s proposal.

In 1996, Lov Grover from Bell Labs invented the quantum search algorithm which yields a quadratic “speed-up” compared to its classical counterpart. A year later the first NMR model for quantum computation was proposed, based on nuclear magnetic resonance techniques. This technique was realized in 1998 with a 2-qubit register, and was scaled up to 7 qubits in the Los Alamos National Lab in 2000.

Starting from 2000 the field saw a tremendous growth. New paradigms of quantum algorithms have appeared, such as adiabatic algorithms, measurement-based algorithms, and topological-quantum-field-theory-based algorithms, as well as new physical models for realizing a large scale quantum computer with cold ion traps, quantum optics (using photons and optical cavity), condensed matter systems and solid state physics (meanwhile, the first NMR model had turned out to be a dead-end with respect to scaling; see DiVincenzo 2000). The basic questions, however, remain open even today: (1) theoretically, can quantum algorithms efficiently solve classically intractable problems? (2) operationally, can we actually realize a large scale quantum computer to run these algorithms?

## 2. Basics

In this section we will review the basic paradigm for quantum algorithms, namely the quantum circuit model, which is composed of the basic quantum units of information (qubits) and the basic logical manipulations thereof (quantum gates).

### 2.1 The Qubit

The qubit is the quantum analogue of the bit, the classical
fundamental unit of information. It is a mathematical object with
specific properties that can be realized physically in many different
ways as an actual physical system. Just as the classical bit has a
*state* (either 0 or 1), a qubit also has a state. Yet contrary
to the classical bit,
\(\lvert 0\rangle\)
and
\(\lvert 1\rangle\)
are but two
possible states of the qubit, and any linear combination
(superposition) thereof is also physically possible. *In
general*, thus, the physical state of a qubit is the superposition
\(\lvert\psi \rangle = \alpha \lvert 0\rangle + \beta \lvert 1\rangle\)
(where \(\alpha\) and \(\beta\) are complex numbers). The state of a qubit
can be described as a vector in a two-dimensional Hilbert space, a
complex vector space
(see the entry on
quantum mechanics).
The special states
\(\lvert 0\rangle\)
and
\(\lvert 1\rangle\)
are known as the *computational basis states*, and form an
orthonormal basis for this vector space. According to quantum theory,
when we try to measure the qubit in this basis in order to determine
its state, we get either
\(\lvert 0\rangle\)
with probability
\(\lvert \alpha\rvert^2\)
or
\(\lvert 1\rangle\)
with probability
\(\lvert \beta\rvert^2\).
Since
\(\lvert \alpha\rvert^2 + \lvert\beta\rvert^2 = 1\)
(i.e., the qubit is a *unit*
vector in the aforementioned two-dimensional Hilbert state), we may
(ignoring the overall phase factor) effectively write its state as
\(\lvert \psi \rangle =\)
cos\((\theta)\lvert 0\rangle + e^{i\phi}\)sin\((\theta)\lvert 1\rangle\),
where the numbers \(\theta\) and \(\phi\) define a point on the unit
three-dimensional sphere, as shown here. This sphere is often called
*the Bloch sphere*, and it provides a useful means to visualize
the state of a single qubit.

\(\lvert 0\rangle\) |

\(\lvert 1\rangle\) |

The Bloch Sphere

Theoretically, a single qubit can store an infinite amount of
information, yet when measured it yields only the classical result (0
or 1) with certain probabilities that are specified by the quantum
state. In other words, the measurement *changes* the state of
the qubit, “collapsing” it from the superposition to one of its
terms. The crucial point is that unless the qubit is measured, the
amount of “hidden” information it stores is conserved under the
dynamic evolution (namely, Schrödinger’s equation). This feature
of quantum mechanics allows one to manipulate the information stored
in unmeasured qubits with quantum gates, and is one of the sources for
the putative power of quantum computers.

To see why, let us suppose we have two qubits at our disposal. If
these were classical bits, then they could be in four possible states
(00, 01, 10, 11). Correspondingly, a pair of qubits has four
*computational basis states*
(\(\lvert 00\rangle\), \(\lvert 01\rangle\), \(\lvert 10\rangle\), \(\lvert 11\rangle)\).
But while a single classical two-bit register can store these numbers
only *one at a time*, a pair of qubits can also exist in a
superposition of these four basis states, each of which with its own
complex coefficient (whose mod square, being interpreted as
a probability, is normalized). As long as the quantum system evolves
unitarily and is unmeasured, all four possible states are
simultaneously “stored” in a single two-qubit quantum register. More
generally, the amount of information that can be stored in a system of
\(n\) unmeasured qubits grows exponentially in \(n\). The
difficult task, however, is to retrieve this information efficiently.

### 2.2 Quantum Gates

Classical computational gates are Boolean logic gates that perform
manipulations of the information stored in the bits. In quantum
computing these gates are represented by matrices, and can be
visualized as rotations of the quantum state on the Bloch sphere. This
visualization represents the fact that quantum gates are unitary
operators, i.e., they preserve the norm of the quantum state (if
\(U\) is a matrix describing a single qubit gate, then
\(U^{\dagger}U=I\), where \(U^{\dagger}\)
is the *adjoint* of \(U\), obtained by transposing and
then complex-conjugating \(U)\). As in the case of classical
computing, where there exists a universal gate (the combinations of
which can be used to compute any computable function), namely, the
*NAND* gate which results from performing an *AND* gate
and then a *NOT* gate, in quantum computing it was shown
(Barenco *et al.*, 1995) that any multiple qubit logic gate may
be composed from a quantum *CNOT* gate (which operates on a
multiple qubit by flipping or preserving the target bit given the
state of the control bit, an operation analogous to the classical
*XOR*, i.e., the exclusive OR gate) and single qubit gates. One
feature of quantum gates that distinguishes them from classical gates
is that they are reversible: the inverse of a unitary matrix is also a
unitary matrix, and thus a quantum gate can always be inverted by
another quantum gate.

The CNOT Gate

Unitary gates manipulate the information stored in the quantum
register, and in this sense ordinary (unitary) quantum evolution can
be regarded as computation (DiVincenzo 1995 showed how a small set of
single-qubit gates and a two-qubit gate is universal, in the sense
that a circuit combined from this set can approximate to arbitrary
accuracy any unitary transformation of \(n\) qubits). In order to
read the result of this computation, however, the quantum register
must be measured. The measurement gate is a non-unitary gate that
“collapses” the quantum superposition in the register onto one of its
terms with the corresponding probability. Usually this measurement is
done in the computational basis, but since quantum mechanics allows
one to express an arbitrary state as a linear combination of basis
states, provided that the states are orthonormal (a condition that
ensures normalization) one can *in principle* measure the
register in any arbitrary orthonormal basis. This, however, doesn’t
mean that measurements in different bases are efficiently equivalent.
Indeed, one of the difficulties in constructing efficient quantum
algorithms stems exactly from the fact that measurement collapses the
state, and some measurements are much more complicated than
others.

### 2.3 Quantum Circuits

Quantum circuits are similar to classical computer circuits in that
they consist of *wires* and *logical gates*. The wires
are used to carry the information, while the gates manipulate it (note
that the wires do not correspond to physical wires; they may
correspond to a physical particle, a photon, moving from one location
to another in space, or even to time-evolution). Conventionally, the
input of the quantum circuit is assumed to be a computational basis
state, usually the state consisting of all
\(\lvert 0\rangle\).
The output state of the circuit is then measured in the computational
basis, or in any other arbitrary orthonormal basis. The first quantum
algorithms (i.e. Deutsch-Jozsa, Simon, Shor and Grover) were
constructed in this paradigm. Additional paradigms for quantum
computing exist today that differ from the quantum circuit model in
many interesting ways. So far, however, they all have been
demonstrated to be computationally equivalent to the circuit model
(see below), in the sense that any computational problem that can be
solved by the circuit model can be solved by these new models with
only a polynomial overhead in computational resources.

## 3. Quantum Algorithms

Algorithm design is a highly complicated task, and in quantum
computing it becomes even more complicated due to the attempts to
harness quantum mechanical features to reduce the complexity of
computational problems and to “speed-up” computation. Before attacking
this problem, we should first convince ourselves that quantum
computers can be harnessed to perform standard, classical, computation
without any “speed-up”. In some sense this is obvious, given the
belief in the universal character of quantum mechanics, and the
observation that any quantum computation that is diagonal in the
computational basis, i.e., involves no interference between the
qubits, is effectively classical. Yet the demonstration that quantum
circuits can be used to simulate classical circuits is not
straightforward (recall that the former are reversible while the
latter use gates which are inherently irreversible). Indeed, quantum
circuits cannot be used *directly* to simulate classical
computation, but the latter can still be simulated on a quantum
computer using an intermediate gate, namely the *Toffoli*
gate. This gate has three input bits and three output bits, two of
which are control bits, unaffected by the action of the gate. The
third bit is a target bit that is flipped if both control bits are set
to 1, and otherwise is left alone. This gate is reversible (its
inverse is itself), and can be used to simulate all the elements of
the classical irreversible circuit with a reversible
one. Consequently, using the quantum version of the *Toffoli*
gate (which by definition permutes the computational basis states
similarly to the classical *Toffoli* gate) one can simulate,
although rather tediously, irreversible classical logic gates with
quantum reversible ones. Quantum computers are thus capable of
performing any computation which a classical deterministic computer
can do.

What about non-deterministic computation? Not surprisingly, a quantum computer can also simulate this type of computation by using another famous quantum gate, namely the Hadamard gate, which receives as an input the state \(\lvert 0\rangle\) and produces the state \(\bfrac{\lvert 0\rangle + \lvert 1\rangle}{\sqrt{2}}\). Measuring this output state yields \(\lvert 0\rangle\) or \(\lvert 1\rangle\) with 50/50 probability, which can be used to simulate a fair coin toss.

The Hadamard Gate

Obviously, if quantum algorithms could be used only to simulate
classical algorithms, then the technological advancement in
information storage and manipulation, encapsulated in “Moore’s law”,
would have only trivial consequences on computational complexity
theory, leaving the latter unaffected by the physical world. But while
some computational problems will always resist quantum “speed-up” (in
these problems the computation time depends on the input, and this
feature will lead to a violation of unitarity hence to an effectively
classical computation even on a quantum computer—see Myers 1997
and Linden and Popescu 1998), the hope is, nonetheless, that quantum
algorithms may not only simulate classical ones, but that they will
actually *outperform* the latter in some cases, and in so doing
help to re-define the abstract notions of tractability and
intractability and violate the physical Church-Turing thesis, at least
as far as computational complexity is concerned.

### 3.1 Quantum-Circuit-Based Algorithms

The first quantum algorithms were designed to exploit the adequacy of
quantum computation to computational problems which involve
oracles. Oracles are devices which are used to answer questions with a
simple *yes* or *no*. The questions may be as elaborate
as one can make them, the procedure that answers the questions may be
lengthy and a lot of auxiliary data may get generated while the
question is being answered. Yet all that comes out of the oracle is
just *yes* or *no*. The oracle architecture is very
suitable for quantum computers. The reason for this is that, as
stressed above, the read-out of a quantum system is
probabilistic. Therefore if one poses a question the answer to which
is given in the form of a quantum state, one will have to carry out
the computation on an ensemble of quantum computers to get
anywhere. On the other hand if the computation can be designed in such
a way that one does get *yes* or *no* in a
*single* measurement (and some data reduction may be required
to accomplish this), then a *single* quantum computer and a
*single* quantum computation run may suffice.

#### 3.1.1 The Deutsch Oracle

This oracle (Deutsch 1989) answers the following question. Suppose we
have a function \(f : \{0,1\} \rightarrow \{0,1\}\), which can be either
constant or balanced. In this case, the function is constant if
\(f\)(0) \(= f\)(1) and it is balanced if \(f\)(0) \(\ne f\)(1). Classically it would take *two* evaluations of the
function to tell whether it is one or the other. Quantumly, we can
answer this question in *one* evaluation. The reason for this
complexity reduction is, again, the superposition principle. To see
why consider the following quantum algorithm. One can prepare the
input qubits of the Deutsch oracle as the superposition
\(\bfrac{\lvert 0\rangle + \lvert 1\rangle}{\sqrt{2}}\)
(using the Hadamard gate on \(\lvert 0\rangle)\)
and the superposition
\(\bfrac{\lvert 0\rangle - \lvert 1\rangle}{\sqrt{2}}\)
(using the Hadamard gate on
\(\lvert 1\rangle)\).
The oracle is implemented with a quantum circuit which takes
inputs like
\(\lvert x,y\rangle\)
to
\(\lvert x, y\oplus f (x)\rangle\),
where \(\oplus\) is
addition modulo two, which is exactly what a *XOR* gate
does. The first qubit of the output of this oracle is then fed into a
Hadamard gate, and the final output of the algorithm is the state

Since \(f\)(0)\(\oplus f\)(1) is 0 if the function is constant and 1 if the function balanced, a single measurement of the first qubit of the output suffices to retrieve the answer to the question whether the function is constant or balanced. In other words, we can distinguish in one run of the algorithm between the two quantum disjunctions without finding out the truth values of the disjuncts themselves in the computation.

#### 3.1.2 The Deutsch-Jozsa Oracle

This oracle (Deutsch and Jozsa 1992) generalizes the Deutsch oracle
to a function \(f : \{0,1\}^{n} \rightarrow \{0,1\}\). We ask
the same question: is the function constant or balanced. Here balanced
means that the function is 0 on half of its arguments and 1 on the
other half. Of course in this case the function may be neither
constant nor balanced. In this case the oracle doesn’t work: It may
say *yes* or *no* but the answer will be
meaningless. Also here the algorithm allows one to evaluate a global
property of the function in one measurement because the output state
is a superposition of balanced and constant states such that the
balanced states all lie in a subspace orthogonal to the constant
states and can therefore be distinguished from the latter in a single
measurement. In contrast, the best deterministic classical algorithm
would require \(\bfrac{2^{n}}{2}+1\) queries to the oracle in
order to solve this problem.

#### 3.1.3 The Simon Oracle

Suppose we have a Boolean function \(f : \{0,1\}^{n} \rightarrow \{0,1\}^{n}\). The function is supposed to be
2-to-1, i.e., for every value of \(f\) there are always two
\(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\) such
that \(f (\mathbf{x}_{1}) = f (\mathbf{x}_{2})\). The function is also supposed
to be periodic, meaning that there is a binary vector
\(\mathbf{a}\) such that
\(f (\mathbf{x}\oplus \mathbf{a}) = f (\mathbf{x})\), where \(\oplus\) designates addition modulo
2, i.e., \(1 \oplus 1 = 0\). The Simon oracle returns the period
\(\mathbf{a}\) in a number of measurements linear in \(n\),
which is exponentially faster than any classical algorithm (Simon
1994). Simon’s oracle reduces to Deutsch’s *XOR* oracle when
\(n=2\), and can indeed be regarded as an extension of the
latter, in the sense that a global property of a function, in this
case its period, can be evaluated in an efficient number of
measurements, given that the output state of the algorithm is
decomposed into orthogonal subspaces, only one of which contains the
solution to the problem, hence repeated measurements in the
computational basis will be sufficient to determine this subspace.
In other words, Simon’s oracle is another example of a quantum
algorithm which evaluates a disjunction seemingly without determining the truth
values of its disjuncts. For more on the logical analysis of these
first quantum-circuit-based algorithms see Bub (2006b).

#### 3.1.4 Shor’s Algorithm

The three oracles just described, although demonstrating the
potential superiority of quantum computers over their classical
counterparts, nevertheless deal with apparently unimportant
computational problems. Indeed, it is doubtful whether the research
field of quantum computing would have attracted so much attention and
would have evolved to its current status if its merit could be
demonstrated only with these problems. But in 1994, after realizing
that Simon’s oracle can be harnessed to solve a much more interesting
and crucial problem, namely *factoring*, which lies at the
heart of current cryptographic protocols such as the RSA (Rivest
1978), Peter Shor has turned quantum computing into one of the most
exciting research domains in quantum mechanics.

Shor’s algorithm (1994) exploits the ingenious number theoretic
argument that two prime factors \(p,q\) of a positive
integer \(N=pq\) can be found by determining the period
of a function \(f(x) = y^x\ \textrm{mod}\ N\), for any \(y \lt N\)
which has no common factors with
\(N\) other than 1 (Nielsen and Chuang 2000, App. 4). The period
\(r\) of \(f(x)\) depends on \(y\) and
\(N\). Once one knows the period, one can factor \(N\) if
\(r\) is even and \(y^{\,\bfrac{r}{2}} \ne -1\)
mod \(N\), which will be jointly the case with probability
greater than \(\bfrac{1}{2}\) for any \(y\) chosen randomly (if not, one
chooses another value of \(y\) and tries again). The factors of
\(N\) are the greatest common divisors of
\(y^{\,\bfrac{r}{2}} \pm 1\) and \(N\), which can
be found in polynomial time using the well known Euclidean
algorithm. In other words, Shor’s remarkable result rests on the
discovery that the problem of *factoring* reduces to the
problem of finding the period of a certain periodic function
\(f: Z_{n} \rightarrow Z_{N}\), where
\(Z_{n}\) is the additive group of integers mod
\(n\) (Note that \(f(x) = y^{x}\ \textrm{mod}\ N\) so that \(f(x+r) = f(x)\) if
\(x+r \le n\). The function is periodic if \(r\)
divides \(n\) exactly, otherwise it is almost periodic). That
this problem can be solved efficiently by a quantum computer is
demonstrated with Simon’s oracle.

Shor’s result is the most dramatic example so far of quantum
“speed-up” of computation, notwithstanding the fact that
*factoring* is believed to be only in **NP** and
not in **NP**-complete. To verify whether \(n\) is
prime takes a number of steps which is a polynomial in
\(\log_{2}n\) (the binary encoding of a natural
number \(n\) requires \(\log_{2}n\) resources). But
nobody knows how to factor numbers into primes in polynomial time, not
even on a probabilistic Turing machine, and the best classical
algorithms we have for this problem are sub-exponential. This is yet
another open problem in the theory of computational complexity. Modern
cryptography and Internet security protocols such public key and
electronic signature are based on these facts (Giblin 1993): It is
easy to find large prime numbers fast, and it is hard to factor large
composite numbers in any reasonable amount of time. The discovery
that quantum computers can solve *factoring* in polynomial time
has had, therefore, a dramatic effect. The implementation of the
algorithm on a physical machine would have economic, as well as
scientific consequences.

#### 3.1.5 Grover’s Algorithm

Suppose you have met someone who kept her name secret, but revealed
her telephone number to you. Can you find out her name using her
number and a phone directory? In the worst case, if there are
\(n\) entries in the directory, the computational resources
required will be linear in \(n\). Grover (1996) showed how this
task, namely, searching an unstructured database, could be done with a
quantum algorithm with complexity of the order
\(\sqrt{n}\). Agreed, this “speed-up” is more modest than Shor’s
since searching an unstructured database belongs to the class
\(\mathbf{P}\), but contrary to Shor’s case, where the classical
complexity of *factoring* is still unknown, here the
superiority of the quantum algorithm, however modest, is definitely
provable. That this quadratic “speed-up” is also the *optimal*
quantum “speed-up” possible for this problem was proved by Bennett,
Bernstein, Brassard and Vazirani (1997).

Although the purpose of Grover’s algorithm is usually described as
“searching a database”, it may be more accurate to describe it as
“inverting a function”. Roughly speaking, if we have a function
\(y=f(x)\) that can be evaluated on a quantum computer, Grover’s
algorithm allows us to calculate \(x\) when given
\(y\). Inverting a function is related to searching a database
because we could come up with a function that produces a particular
value of \(y\) if \(x\) matches a desired entry in a
database, and another value of \(y\) for other values of
\(x\). The applications of this algorithm are far-reaching (over
and above finding the name of the mystery ‘date’
above). For example, it can be used to to determine efficiently the
number of solutions to an \(N\)-item search problem, hence to
perform exhaustive searches on a class of solutions to an
**NP**-complete problem and substantially reduce the
computational resources required for solving it.

### 3.2 Adiabatic Algorithms

More than a decade has passed since the discovery of the first
quantum algorithm, but so far little progress has been made with
respect to the “Holy Grail” of solving an **NP**-complete
problem with a quantum-circuit model. As stressed above, Shor’s
algorithm stands alone in its exponential “speed-up”, yet while no
efficient classical algorithm for *factoring* is *known*
to exist, there is also no proof that such an algorithm doesn’t or
cannot exist. In 2000 a group of physicists from MIT and Northeastern
University (Farhi *et al.* 2000) proposed a novel paradigm for
quantum computing that differs from the circuit model in several
interesting ways. Their goal was to try to solve with this algorithm
an instance of *satisfiability*—deciding whether a
proposition in the propositional calculus has a satisfying truth
assignment—which is one of the most famous
**NP**-complete problems (Cook 1971).

According to the adiabatic theorem (e.g., Messiah 1961) and given certain specific conditions, a quantum system remains in its lowest energy state, known as the ground state, along an adiabatic transformation in which the system is deformed slowly and smoothly from an initial Hamiltonian to a final Hamiltonian (as an illustration, think of moving a baby who is sleeping in a cradle from the living room to the bedroom. If the transition is done slowly and smoothly enough, and if the baby is a sound sleeper, then it will remain asleep during the whole transition). The most important condition in this theorem is the energy gap between the ground state and the next excited state (in our analogy, this gap reflects how sound asleep the baby is). Being inversely proportional to the evolution time \(T\), this gap controls the latter. If this gap exists during the entire evolution (i.e., there is no level crossing between the energy states of the system), the theorem dictates that in the adiabatic limit (when \(T\rightarrow \infty)\) the system will remain in its ground state. In practice, of course, \(T\) is always finite, but the longer it is, the less likely it is that the system will deviate from its ground state during the time evolution.

The crux of the quantum adiabatic algorithm which rests on the
adiabatic theorem lies in the possibility of encoding a specific
instance of a given decision problem in a certain Hamiltonian (this
can be done by capitalizing on the well-known fact that any decision
problem can be derived from an optimization problem by incorporating
into it a numerical bound as an additional parameter). One then starts
the system in a ground state of another Hamiltonian which is easy to
construct, and slowly evolves the system in time, deforming it towards
the desired Hamiltonian. According to the quantum adiabatic theorem
and given the gap condition, the result of such a physical process is
another energy ground state that encodes the solution to the desired
decision problem. The adiabatic algorithm is thus a rather ‘laid
back’ algorithm: one needs only to start the system in its
ground state, deform it adiabatically, and measure its final ground
state in order to retrieve the desired result. But whether or not this
algorithm yields the desired “speed-up” depends crucially on the
behavior of the energy gap as the number of degrees of freedom in the
system increases. If this gap decreases exponentially with the size of
the input, then the evolution time of the algorithm will increase
exponentially; if the gap decreases polynomially, the decision problem
so encoded could be solved efficiently in polynomial time. Although
physicists have been studying spectral gaps for almost a century, they
have never done so with quantum computing in mind. How this gap
behaves *in general* remains thus far an open empirical
question.

The quantum adiabatic algorithm holds much promise (Farhi
*et al.* 2001), and recently it was shown (Aharonov *et
al.* 2004) to be polynomially equivalent to the circuit model
(that is, each model can simulate the other with only polynomial,
i.e., modest, overhead of resources, namely, number of qubits and
computational steps), but the caveat that is sometimes left
unmentioned is that its application to an intractable computational
problem may sometimes require solving another, as intractable a task
(this general worry was first raised by a philosopher; see Pitowsky
1990). Indeed, Reichardt (2004) has shown that there are simple
problems for which the algorithm will get stuck in a local minimum, in
which there are exponentially many eigenvalues all exponentially close
to the ground state energy, so applying the adiabatic theorem, even
for these simple problems, will take exponential time, and we are back
to square one.

### 3.3 Measurement-Based Algorithms

Measurement-based algorithms differ from the circuit model in that instead of applying unitary evolution as the basic mechanism for the manipulation of information, these algorithms use only non-unitary measurements as their computational steps. These models are especially interesting from a foundational perspective because they have no evident classical analogues and because they offer a new insight on the role of entanglement in quantum computing (Jozsa 2005). They may also have interesting consequences for experimental considerations, suggesting a different kind of computer architecture which is more fault tolerant (Nielsen and Dawson 2004).

The measurement-based algorithms fall into two categories. The first
is teleportation quantum computing (based on an idea of Gottesman and
Chuang 1999, and developed into a computational model by Nielsen 2003
and Leung 2003). The second is the “one way quantum
computer”, known also as the “cluster state” model
(Raussendorf and Briegel 2000). The interesting feature of these
models is that they are able to represent arbitrary quantum dynamics,
including unitary dynamics, with basic non-unitary measurements. The
measurements are performed on a pool of highly entangled states (the
amount of entanglement needed is still under dispute), and are
adaptive, i.e., each measurement is done in a different basis which is
calculated classically, given the result of the earlier measurement
(the first model uses measurements of 2 or more qubits, while the
second uses only single qubit measurements; in the first model only
bi-partite entanglement is used, while the second one has
multi-partite entanglement across all qubits). Such exotic models
might seem redundant, especially when they have been shown to be
polynomially equivalent to the standard circuit model in terms of
computational complexity (Raussendorf *et al.* 2003). Their
merit, however, lies in the foundational lessons they drive home: with
these models the separation between the classical part (i.e., the
calculation of the next measurement-basis) and the quantum parts
(i.e., the measurement and the entangled states) of the computation
becomes evident, hence it may be easier to pinpoint the quantum
resources that are responsible for the putative
“speed-up”.

### 3.4 Topological-Quantum-Field-Theory (TQFT) Algorithms

Another exotic model for quantum computing which is attracting a lot
of attention lately, especially from Microsoft inc. (Freedman 1998),
is the Topological Quantum Field Theory model. In contrast to the
straightforward and standard circuit model, this model resides in the
most abstract reaches of theoretical physics. The exotic physical
systems TQFT describes are topological states of matter. That the
formalism of TQFT can be applied to computational problems was shown
by Witten (1989) and the idea was later developed by others. Also here
the model was proved to be efficiently simulated on a standard quantum
computer (Freedman, Kitaev, Wang 2000, Aharonov *et al.* 2005),
but its merit lies in its high tolerance to errors resulting from any
possible realization of a large scale quantum computer (see
below). Topology is especially helpful here because many global
topological properties are, by definition, invariant under
deformation, and given that most errors are local, information encoded
in topological properties is robust against them.

### 3.5 Realizations

The quantum computer might be the theoretician’s dream, but as far as experimentalists are concerned, its realization is a nightmare. The problem is that while some prototypes of the simplest elements needed to build a quantum computer have already been implemented in the laboratory, it is still an open question how to combine these elements into scalable systems. Shor’s algorithm may break the RSA code, but it will remain an anecdote if the largest number that it can factor is 15. In the circuit-based model the problem is to achieve a scalable quantum system that at the same time will allow one to (1) robustly represent quantum information, (2) perform a universal family of unitary transformation, (3) prepare a fiducial initial state, and (4) measure the output result. Alternative paradigms may trade some of these requirements with others, but the gist will remain the same, i.e., one would have to achieve control of one’s quantum system in such a way that the system will remain “quantum” albeit macroscopic or even mesoscopic in its dimensions.

In order to deal with these requirements, several ingenious solutions
were devised, including quantum error correction codes (Shor 1995) and
fault tolerant computation (Shor and DiVincenzo 1996, Aharonov and
Ben-Or 1997) that dramatically reduce the spread of errors during a
‘noisy’ quantum computation. The problem with these active
error correction schemes is that they were devised for a very
unrealistic noise model which treats the computer as quantum and the
environment as classical (Alicki, Lidar & Zanardi 2006) Once a
more realistic noise model is allowed, the feasibility of large scale,
fault tolerant and computationally superior quantum computers is less
clear (Hagar 2009). Another scheme to reduce errors in the
implementation of quantum algorithms on large scale quantum computers
is to encode information in noiseless subsystems, or decoherence free
subspaces (Lidar, Chuang & Whaley 1998). This strategy seems more
promising from a physical point of view, yet also here the question of
how those noiseless subspaces scale with the size of the computer
remains open. If one hopes to solve intractable problems efficiently
with a scalable quantum computer, then the construction of the
theoretical operator that measures a quantum state which encodes a
solution to an **NP**-hard problem should not require an
exponential time, or solving yet another **NP**-hard
problem.

Finally, as the implementation of Shor’s algorithm on a large scale quantum computer seems still beyond our reach, quantum information scientists turn to the original goal of using quantum computers to simulate quantum systems. While Feynman’s conjecture is still unproven, complexity theorists attempt to narrow the gap between what they believe is true about quantum mechanics, namely, that it’s exponentially-hard to simulate on a classical computer, and what experimentalists can currently demonstrate (e.g., Aaronson & Arkhipov 2010).

## 4. Philosophical Implications

### 4.1 What is *Quantum* in Quantum Computing?

Notwithstanding the excitement around the discovery of Shor’s
algorithm, and apart from the almost insurmountable problem of
practically realizing and implementing a large scale quantum computer,
a crucial theoretical question remains open, namely, what physical
resources are responsible for the putative power of quantum computing?
Put another way, what are the essential features of quantum mechanics
that allow one to solve problems or simulate certain systems far more
efficiently than on a classical computer? Remarkable is also the fact
that the relevance of features commonly thought essential to the
superiority of quantum computers, e.g., entanglement and interference
(Josza 1997), has recently been questioned (Linden and Popescu 1999,
Biham 2004). Moreover even if these features \(do\) play an
essential role in the putative quantum “speed-up”, one
must still answer the question of *how* they do so (Fortnow
2003, Cuffaro forthcoming).

Theoretical as it may seem, the question “what is *quantum* in
quantum computing?” has an enormous practical consequence. One of the
embarrassments of quantum computing is the fact that, so far, only one
algorithm has been discovered, namely Shor’s, for which a quantum
computer is significantly faster than any *known* classical
one. It is almost certain that one of the reasons for this scarcity of
quantum algorithms is related to the lack of our understanding of what
makes a quantum computer quantum (see also Preskill 1998 and Shor
2004). As an ultimate answer to this question one would like to have
something similar to Bell’s (1964) famous
theorem,
i.e., a succinct crispy statement of the fundamental difference
between quantum and classical systems, encapsulated in the
non-commutative character of observables. Quantum computers,
unfortunately, do not seem to allow such simple
characterization. Observables—in the quantum circuit model there
are only two, the preparation of the initial state and the observation
of the final state, in the same basis, and of the same variable, at
the end of the computation—are not as important here as in
Bell’s case since any measurement commutes with itself. The
non-commutativity in quantum computing lies much deeper, and it is
still unclear how to cash it into useful currency. Quantum computing
skeptics (Levin 2003) happily capitalize on this puzzle: If no one
knows *why* quantum computers are superior to classical ones,
how can we be sure that they *are*, indeed, superior?

#### 4.1.1 The Debate over Parallelism and Many Worlds

One well-known answer to the question, popularized by Deutsch (1997), takes its motivation from the circumstance that in some quantum circuit model algorithms, there are steps in which it appears as though functions are evaluated for each of their possible input values simultaneously. Something like the following transformation, for instance, is typical (note that normalization factors have been omitted):

\[\tag{1} \Sigma_{x} \lvert x\rangle \lvert 0\rangle \rightarrow \Sigma_{x} \lvert x\rangle \lvert f(x)\rangle. \]
The idea that we should take this at face value—that quantum
computers *actually do* compute a function for many different input
values simultaneously—is what Duwell (2007) calls the *Quantum
Parallelism Thesis* (QPT). For Deutsch, who accepts it
as true, the only reasonable explanation for the QPT is that
the many worlds interpretation (MWI) of quantum mechanics is also
true. For Deutsch, a quantum computer in superposition, like any other
quantum system, exists in some sense in many classical universes
simultaneously. These provide the physical arena within which the
computer effects its parallel computations. This conclusion is also
endorsed by Hewitt-Horsman (2009) and by Wallace (2012). Wallace
notes, however, that the QPT—and hence the explanatory need for
many worlds—may not be true of all or even most quantum
algorithms, although Wallace takes it to be true of Shor’s.

For Steane (2003), in contrast, quantum computers are not well described in terms of many worlds or even quantum parallelism. Among other things, Steane argues that the motivation for the QPT is at least partly due to misleading aspects of the standard quantum formalism, for some classical systems can be similarly described so as to suggest parallelism. Further, the Gottesman-Knill theorem (Nielsen and Chuang, 2000) shows that many algorithms, which suggest parallelism when written in standard notation, can be re-expressed in an alternative notation so as to lend themselves straightforwardly to an efficient classical simulation. Additionally, comparing the information actually produced by quantum and classical algorithms (state collapse entails that only one evaluation instance in (1) is ever accessible, while a classical computer must actually produce every instance) suggests that quantum algorithms perform not more but fewer, cleverer, computations than classical algorithms (see, also, 4.1.2 below).

Another critic is Duwell (2007), who (contra Steane) accepts the QPT,
but nevertheless denies that it uniquely supports the MWI. Considering
the phase relations between the terms in a superposition such as (1)
is crucially important when evaluating a quantum algorithm’s
computational efficiency. Phase relations, however, are global
properties of a state. Thus a quantum computation, Duwell argues, does
not consist *solely* of local parallel computations. But in this
case, the QPT does not uniquely support the MWI over other
explanations.

Defending the MWI, Hewitt-Horsman (2009) argues (contra Steane) that to state that quantum computers do not actually generate each of the evaluation instances represented in (1) is false according to the view: on the MWI such information could be extracted in principle given sufficiently advanced technology. Further, Hewitt-Horsman emphasizes that the MWI is not motivated simply by a suggestive mathematical representation. Unlike in Steane’s ‘classical parallelism’ examples, worlds on the MWI are defined by their explanatory usefulness, manifested in particular by their stability and independence over the time scales relevant to the computation. Wallace (2012) argues similarly.

Cuffaro (2012) and Aaronson (2013) point out that the Many Worlds
Explanation of Quantum Computing (MWQC) and the MWI are not actually
identical. The latter employs
decoherence
as a criterion for distinguishing macroscopic worlds from one
another. Quantum circuit model algorithms, however,
utilize *coherent* superpositions. To distinguish computational
worlds, therefore, one must weaken the decoherence criterion, but
Cuffaro argues that this move is ad hoc. Further, Cuffaro argues that
the MWQC is for all practical purposes incompatible
with measurement based computation, for even
granting a weakened world identification criterion, there is no
natural way in this model to identify worlds that are stable and
independent in the way required.

#### 4.1.2 The Elusive Nature of Speed-Up

Even if we could rule out the MWQC, the problem of finding the physical resource(s)
responsible for quantum “speed-up” would remain a difficult one. Consider a
solution of a decision problem, say
*satisfiability*, with a quantum algorithm based on the circuit
model. What we are given here as input is a proposition in the
propositional calculus and we have to decide whether it has a
satisfying truth assignment. As Pitowsky (2002) shows, the quantum
algorithm appears to solve this problem by testing all
\(2^{n}\) assignments “at once” as suggested
by (1), yet this quantum ‘miracle’ helps us very little
since, as previously mentioned, any measurement performed on the
output state collapses it, and if there is one possible truth
assignment that solves this decision problem, the probability of
retrieving it is \(2^{-n}\), just as in the case of
a classical probabilistic Turing machine which guesses the solution
and then checks it. Pitowsky’s conclusion (echoed, as we saw, by
Steane (2003) and Duwell (2007)) is that in order to enhance
computation with quantum mechanics we must construct
‘clever’ superpositions that increase the probability of
successfully retrieving the result far more than that of a pure
guess. Shor’s algorithm and the class of algorithms that evaluate a
*global* property of a function (this class is known as the
*hidden subgroup* class of algorithms) are (so far) a unique
example of both a construction of such ‘clever’
superposition and a retrieval of the solution in polynomial time. The
quantum adiabatic algorithm may give us similar results, contingent
upon the existence of an energy gap that decreases polynomially with
the input.

This question also raises important issues about how to measure the
complexity of a given quantum algorithm. The answer differs, of
course, according to the particular model at hand. In the adiabatic
model, for example, one needs only to estimate the energy gap behavior
and its relation to the input size (encoded in the number of degrees
of freedom of the Hamiltonian of the system). In the measurement-based
model, one counts the number of measurements needed to reveal the
solution that is hidden in the input cluster state (since the
preparation of the cluster state is a polynomial process, it does not
add to the complexity of the computation). But in the circuit model
things are not as straightforward. After all, the whole of the
quantum-circuit-based computation can be be simply represented as a
*single* unitary transformation from the input state to the
output state.

This feature of the quantum circuit model supports the conjecture
that the power of quantum computers, if any, lies not in quantum
dynamics (i.e., in the Schrödinger equation), but rather in the
quantum state, or the wave function. Another argument in favor of this
conjecture is that the Hilbert subspace “visited” during a quantum
computational process is, at any moment, a linear space spanned by all
of the vectors in the total Hilbert space which have been created by
the computational process up to that moment. But this Hilbert subspace
is thus a subspace spanned by a polynomial number of vectors and is
thus at most a polynomial subspace of the total Hilbert space. A
classical simulation of the quantum evolution on a Hilbert space with
polynomial number of dimensions (that is, a Hilbert space spanned by a
number of basis vectors which is polynomial in the number of qubits
involved in the computation), however, can be carried out in a
polynomial number of classical computations. Were quantum
*dynamics* the sole ingredient responsible to the efficiency of quantum computing,
the latter could be mimicked in a polynomial number of steps with a
classical computer (see, e.g., Vidal 2003).

This is not to say that quantum computation is no more powerful
than classical computation. The key point, of course,
is that one does not end a quantum computation with an arbitrary
superposition, but aims for a very special, ‘clever’
state—to use Pitowsky’s term. Quantum computations may not
*always* be mimicked with a classical computer because the
characterization of the computational subspace of certain quantum
states is difficult, and it seems that these special,
‘clever’, quantum states cannot be classically represented
as vectors derivable via a quantum computation in an optimal basis, or
at least that one cannot do so in such way that would allow one to
calculate the outcome of the final measurement made on these states.

Consequently, in the quantum circuit model one should count the
number of computational steps in the computation not by counting the
number of transformations of the state, but by counting the number of
one- or two-qubit local transformations that are required to create
the ‘clever’ superposition that ensures the desired
“speed-up”. (Note that Shor’s algorithm, for example, involves three
major steps in this context: First, one creates the
‘clever’ entangled state with a set of unitary
transformations. The result of the computation—a global property
of a function—is now ‘hidden’ in this state; second,
in order to retrieve this result, one projects it on a subspace of the
Hilbert space, and finally one performs another set of unitary
transformations in order to make the result measurable in the original
computational basis. *All* these steps count as
*computational* steps as far as the efficiency of the algorithm
is concerned. See also Bub 2006a.) The trick is to perform these local
one- or two-qubit transformations in polynomial time, and it is likely
that it is here where the physical power of quantum computing may be
found.

### 4.2 Experimental Metaphysics?

The quantum information revolution has prompted several physicists and philosophers to claim that new insights can be gained from the rising new science into conceptual problems in the foundations of quantum mechanics (Fuchs 2002, Bub 2005). Yet while one of the most famous foundational problems in quantum mechanics, namely the quantum measurement problem, remains unsolved even within quantum information theory (see Hagar 2003 and Hagar and Hemmo 2006 for a critique of the quantum information theoretic approach to the foundations of quantum mechanics and the role of the quantum measurement problem in this context), some quantum information theorists dismiss it as a philosophical quibble (Fuchs 2002). Indeed, in quantum information theory the concept of “measurement” is taken as a primitive, a “black box” which remains unanalyzed. The measurement problem itself, furthermore, is regarded as a misunderstanding of quantum theory. But recent advances in the realization of a large scale quantum computer may eventually prove quantum information theorists wrong: Rather than supporting the dismissal of the quantum measurement problem, these advances may surprisingly lead to its empirical solution.

The speculative idea is the following. As it turns out,
collapse theories— one form of
alternatives to quantum theory which aim to solve the measurement
problem—modify Schrödinger’s equation and give different
predictions from quantum theory in certain specific
circumstances. These circumstances may be realized, moreover, if
decoherence
effects could be suppressed (Bassi *et al.* 2005). Now one of
the most difficult obstacles that await the construction of a large
scale quantum computer is its robustness against decoherence effects
(Unruh 1995). It thus appears that the technological capabilities
required for the realization of a large scale quantum computer are
exactly those upon which the distinction between “true” and “false”
collapse (Pearle 1998), i.e., between collapse theories and
environmentally induced decoherence, is contingent. Consequently,
while quantum computing may elucidate the essential distinction
between quantum and classical physics, its physical realization would
shed light also on one of the long standing conceptual problems in the
foundations of the theory, and would serve as yet another example of
experimental metaphysics (the term was coined by Abner Shimony to
designate the chain of events that led from
the EPR argument
via
Bell’s theorem
to Aspect’s experiments).

### 4.3 Are There Computational Kinds?

Another philosophical implication of the realization of a large scale
quantum computer regards the long-standing debate in the philosophy of
mind on the autonomy of computational theories of the mind (Fodor
1974). In the shift from strong to weak artificial intelligence, the
advocates of this view tried to impose constraints on computer
programs before they could qualify as theories of cognitive science
(Pylyshyn 1984). These constraints include, for example, the nature of
physical realizations of symbols and the relations between abstract
symbolic computations and the physical causal processes that execute
them. The search for the computational feature of these theories,
i.e., for what makes them *computational* theories of the mind,
involved isolating some features of the computer *as such*. In
other words, the advocates of weak AI were looking for computational
properties, or kinds, that would be *machine independent*, at
least in the sense that they would not be associated with the physical
constitution of the computer, nor with the specific machine model that
was being used. These features were thought to be instrumental in
debates within cognitive science, e.g., the debate between
functionalism and connectionism (Fodor and Pylyshyn 1988).

Note, however, that once the physical Church-Turing thesis is violated, some computational notions cease to be autonomous. In other words, given that quantum computers may be able to efficiently solve classically intractable problems, hence re-describe the abstract space of computational complexity, computational concepts and even computational kinds such as ‘an efficient algorithm’ or ‘the class NP’, will become machine-dependent, and recourse to ‘hardware’ will become inevitable in any analysis thereof.

Advances in quantum computing may thus militate against the
functionalist view about the *unphysical* character of the
types and properties that are used in computer science. In fact, these
types and categories may *become* physical as a result of this
natural development in physics (e.g., quantum computing, chaos
theory). Consequently, efficient quantum algorithms may also serve as
counterexamples to a-priori arguments against reductionism (Pitowsky
1996).

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## Other Internet Resources

### Online Papers

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### Web sites of interest

- The Quantum Computer Project, MIT
- Institute of Quantum Computing, Canada
- Anton Zeilinger’s Quantum Information Group, University of Vienna
- Samuel Braunstein’s Quantum computation: a tutorial, University of York
- Umesh Vazirani’s Quantum Computation Course, University of California/Berkeley
- Isaac Chuang’s Course on Quantum Infromation Theory, MIT