nLab quantum computation

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Contents

Context

Computation

constructive mathematics, realizability, computability

intuitionistic mathematics

propositions as types, proofs as programs, computational trinitarianism

Constructive mathematics

Realizability

Computability

Quantum systems

quantum logic


quantum physics


quantum probability theory – observables and states


quantum information


quantum computation

quantum algorithms:

Contents

Idea

General

Quantum computation is computation in terms of quantum information theory, possibly implemented on quantum computers, hence on physical systems for which phenomena of quantum mechanics are not negligible: quantum systems such as consisting of fundamental particles like photons or electrons but also often of quantum-quasiparticles such as Cooper pairs in superconductors.

In terms of the computational trilogy, quantum computation is the computation corresponding to (some kind of) quantum logic/(dependent) linear type theory.

To the extent that quantum computation involves quantum measurement (generally to read out results, but often also as a kind of quantum logic gate in its own right) it is a form of nondeterministic computation, due to the intrinsic stochastic nature of the quantum state collapse during quantum measurement.

However, away from quantum measurement, quantum gates are understood to implement unitary operators on a given Hilbert space of quantum states (fundamentally due to the unitary nature of time-evolution as given by the Schrödinger equation), for which quantum computation is hence not only deterministic but in fact a form of reversible computation. This relates to its susceptibility to noise and the practical need for quantum error correction and/or topological quantum stabilization.

Specifically, topological quantum computation is (or is meant to be) quantum computation implemented on physical systems governed by topological quantum field theory, such as Chern-Simons theory, thought to describe for instance anyon defects in topologically ordered gapped ground states of quantum materials in topological phases of matter.

Classical control, quantum data

Any practical quantum computer will be classically controlled (Knill 96, Ömer 03, Nagarajan, Papanikolaou & Williams 07, Devitt 14, in line with the general idea of parameterized quantum systems):

From Miszczak 11

But in general the classical control itself may be conditioned on quantum measurement-outcomes available only during runtime, a more subtle mechanism that (at least in the Quipper-community) has come to be called “dynamic lifting”:

SQRAM model adapted from Nagarajan, Papanikolaou Williams 2007, Fig 1

See the list of references below.

The paradigm of classically controlled quantum computation applies in particular (Kim & Swingle 17) to the currently and near-term available noisy intermediate-scale quantum (NISQ) computers (Preskill 18, see the references below), which are useful for highly specialized tasks (only) and need to be emdedded in and called from a more comprehensive classical computing environment.

This, in turn, applies particularly to applications like quantum machine learning (Benedetti, Lloyd, Sack & Fiorentini 19, TensorFlow Quantum, for more see the references there).

Quantum languages and quantum circuits

A natural way to understand (via computational trilogism) quantum programming languages is as linear logic/linear type theory (Pratt 92, for more see at quantum logic) with categorical semantics in non-cartesian symmetric monoidal categories (Abramsky & Coecke 04, Abramsky & Duncan 05, Duncan 06, Lago-Faffian 12). .

The corresponding string diagrams are known as quantum circuit diagrams.

In fact, languages for classically controlled quantum computation should be based on dependent linear type theory (Vakar 14, Vakar 15, Vakar 17, Sec. 3, Lundfall 17, Lundfall 18, following Schreiber 14) with categorical semantics in indexed monoidal categories:

classical controlquantum data
intuitionistic typesdependent linear types

This idea of classically controlled quantum programming via dependent linear type theory has been implemented in QML and in Quipper in FKS 20, FKRS 20.

Examples

References

General

The idea of quantum computation was first expressed in:

  • Yuri I. Manin, Introduction to: Computable and Uncomputable, Sov. Radio (1980) [Russian original: pdf], Enlish translation on p. 69-77 of Mathematics as Metaphor: Selected essays of Yuri I. Manin, Collected Works 20, AMS (2007) [ISBN:978-0-8218-4331-4]

    Perhaps, for a better understanding of [molecular biology], we need a mathematical theory of quantum automata.

  • Paul Benioff, The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines, J Stat Phys 22, 563–591 (1980) [doi:10.1007/BF01011339]

  • Paul Benioff, Quantum Mechanical Models of Turing Machines That Dissipate No Energy, Phys. Rev. Lett. 48 1581 (1982) [doi:10.1103/PhysRevLett.48.1581]

  • Richard Feynman, Simulating physics with computers, Int J Theor Phys 21, 467–488 (1982) (doi:10.1007/BF02650179)

    because nature isn’t classical, dammit, if you want to make a simulation of nature, you’d better make it quantum mechanical

The notion of quantum logic gates and quantum circuits was first made explicit in:

The terminology q-bit goes back to:

The potential practical relevance of quantum computation was highlighted early on via toy examples

such as the Deutsch-Jozsa algorithm:

and became widely appreciated in 1996/1997 with the discovery of several practically interesting algorithms exhibiting “quantum supremacy”, such as:

in quantum chemistry

in search algorithms (Grover's algorithm)

and in (quantum) cryptography (Shor's algorithm)

Review:

Quantum computation became widely thought to be not just a theoretical but a plausible practical possibility with the theoretical understanding of quantum error correction (which however remains to be implemented scalably):

Early review:

An alternative/parallel approach to avoiding errors due to decoherence is topological quantum computation, see there for references.

Toy quantum computers have been realized (see references below) and have been argued to demonstrate quantum supremacy (see there for references) but existing machines remain “noisy and intermediate scale” (see the references below).

Textbook accounts on quantum computation:

Introducing the notion of quantum supremacy and highlighting its relation to quantum entanglement:

  • John Preskill: Quantum computing and the entanglement frontier: pp. 63-80 in: The Theory of the Quantum World – Proceedings of the 25th Solvay Conference on Physics, World Scientific (2013) [arXiv:1203.5813, doi:10.1142/8674, slides: pdf]

Further introduction and survey:

See also:

Experimental demonstration of “quantum supremacy” (“quantum advantage”):

Review:

Potential relation to the Unruh effect?:

  • Eric W. Aspling, John A. Marohn, Michael J. Lawler, Design Constraints for Unruh-DeWitt Quantum Computers [arXiv:2210.12552]

Quantum programming languages

The first proposal towards formalizing quantum programming languages was:

On quantum programming languages (programming languages for quantum computation):

General:

See also:

Surveys of existing languages:

  • Simon Gay, Quantum programming languages: Survey and bibliography, Mathematical Structures in Computer Science16(2006) (doi:10.1017/S0960129506005378, pdf)

  • Sunita Garhwal, Maryam Ghorani , Amir Ahmad, Quantum Programming Language: A Systematic Review of Research Topic and Top Cited Languages, Arch Computat Methods Eng 28, 289–310 (2021) (doi:10.1007/s11831-019-09372-6)

  • B. Heim et al., Quantum programming languages, Nat Rev Phys 2 (2020) 709–722 [[doi:10.1038/s42254-020-00245-7]]

Quantum programming via quantum logic understood as linear type theory interpreted in symmetric monoidal categories:

The corresponding string diagrams are known in quantum computation as quantum circuit diagrams:

Typedfunctional programming languages for quantum computation:

QPL:

QML:

quantum lambda calculus?:

quantum IO monad:

Quipper:

QWIRE:

CoqQ:


On Q Sharp:

  • Kartik Singhal, Kesha Hietala, Sarah Marshall, Robert Rand, Q# as a Quantum Algorithmic Language, Proceedings of Quantum Physics and Logic (2022) [[arXiv:2206.03532]]

On classically controlled quantum computation:

Quantum programming via dependent linear type theory/indexed monoidal (∞,1)-categories:

specifically with Quipper:

On quantum software verification:

with Quipper:

  • Linda Anticoli, Carla Piazza, Leonardo Taglialegne, Paolo Zuliani, Towards Quantum Programs Verification: From Quipper Circuits to QPMC, In: Devitt S., Lanese I. (eds) Reversible Computation. RC 2016. Lecture Notes in Computer Science, vol 9720. Springer, Cham (doi:10.1007/978-3-319-40578-0_16)

with QWIRE:

with CoqQ: Ying et al. (2022)

Classically controlled quantum computing

Theory of classically controlled quantum computing and parameterized quantum circuits:

Application of classically controlled quantum computation:

  • Isaac H. Kim, Brian Swingle, Robust entanglement renormalization on a noisy quantum computer (arXiv:1711.07500)

    (in terms of holographic tensor network states)

  • Sukin Sim, Peter D. Johnson, Alan Aspuru-Guzik, Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms, Adv. Quantum Technol. 2 (2019) 1900070 (arXiv:1905.10876)

  • Mateusz Ostaszewski, Edward Grant, Marcello Benedetti, Structure optimization for parameterized quantum circuits, Quantum 5, 391 (2021) (arXiv:1905.09692)

  • Ruslan Shaydulin et al., A Hybrid Approach for Solving Optimization Problems on Small Quantum Computers (doi:10.1109/MC.2019.2908942)

  • Eneko Osaba, Esther Villar-Rodriguez, Izaskun Oregi, Aitor Moreno-Fernandez-de-Leceta, Focusing on the Hybrid Quantum Computing – Tabu Search Algorithm: new results on the Asymmetric Salesman Problem (arXiv:2102.05919)

in particular in quantum machine learning:

  • Marcello Benedetti, Erika Lloyd, Stefan Sack, Mattia Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Science and Technology 4, 043001 (2019) (arXiv:1906.07682)

  • D. Zhu et al. Training of quantum circuits on a hybrid quantum computer, Science Advances, 18 Oct 2019: Vol. 5, no. 10, eaaw9918 (doi: 10.1126/sciadv.aaw9918)

  • Andrea Mari, Thomas R. Bromley, Josh Izaac, Maria Schuld, Nathan Killoran, Transfer learning in hybrid classical-quantum neural networks, Quantum 4, 340 (2020) (arXiv:1912.08278)

  • Thomas Hubregtsen, Josef Pichlmeier, Patrick Stecher, Koen Bertels, Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability, Quantum Machine Intelligence volume 3, Article number: 9 (2021) (arXiv:2003.09887, doi:10.1007/s42484-021-00038-w)

  • TensorFlow.org (Google), Hybrid quantum classical models

Noisy intermediate-scale quantum computing

  • Nikolaj Moll et al., Quantum optimization using variational algorithms on near-term quantum devices, Quantum Science and Technology, Volume 3, Number 3 2018 (arXiv:1710.01022)

  • John Preskill, Quantum Computing in the NISQ era and beyond, Quantum 2 79 (2018) [arXiv:1801.00862, doi:10.22331/q-2018-08-06-79]

  • Daniel Koch, Brett Martin, Saahil Patel, Laura Wessing, and Paul M. Alsing, Demonstrating NISQ era challenges in algorithm design on IBM’s 20 qubit quantum computer, AIP Advances 10, 095101 (2020) (doi:10.1063/5.0015526)

  • Frank Leymann, Johanna Barzen, The bitter truth about gate-based quantum algorithms in the NISQ era, Quantum Sci. Technol. 5 044007 (2020) (doi:10.1088/2058-9565/abae7d)

Quantum programming via monads

Discussion of aspects of quantum programming in terms of monads in functional programming are in

As linear logic

Discussion of quantum computation as the internal linear logic/linear type theory of compact closed categories is in

An exposition along these lines is in

  • John Baez, Mike Stay, Physics, topology, logic and computation: a rosetta stone, arxiv/0903.0340; in “New Structures for Physics”, ed. Bob Coecke, Lecture Notes in Physics 813, Springer, Berlin, 2011, pp. 95-174

Quantum information theory via String diagrams

The observation that a natural language for quantum information theory and quantum computation, specifically for quantum circuit diagrams, is that of string diagrams in †-compact categories (see quantum information theory via dagger-compact categories):

On the relation to quantum logic/linear logic:

Early exposition with introduction to monoidal category theory:

With further emphasis on quantum computation:

Generalization to quantum operations on mixed states (completely positive maps of density matrices):

Formalization of quantum measurement via Frobenius algebra-structures:

Textbook accounts (with background on relevant monoidal category theory):

Refinement to the ZX-calculus for specific control of quantum circuit-diagrams:

Relating the ZX-calculus to braided fusion categories for anyon braiding:

Topological quantum computing

topological quantum computation is discussed in

Relation to tensor networks

Relation to tensor networks, specifically matrix product states:

  • Yiqing Zhou, E. Miles Stoudenmire, Xavier Waintal, What limits the simulation of quantum computers?, arXiv:2002.07730

Laboratory realization

The very first proof-of-principle quantum computations were made with nuclear magnetic resonance-technology:

See also:

Realization with superconducting qbits:

(…)

Realization in photonics:

  • Han-Shen Zhong et al. Quantum computational advantage using photons, Science 370, n. 6523 (2020) 1460-1463 doi

(…)

Commercial quantum computers

Since 2019, NISQ quantum computers can be accessed through cloud services:

There exist toy desktop quantum computers for educational purposes, operating on a couple of nuclear magnetic resonance qbits at room temperature :

category: computer science, physics

Last revised on January 2, 2023 at 10:48:59. See the history of this page for a list of all contributions to it.

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