Photonic

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Light is the first system where humans observed quantum phenomena and remains in many ways the best understood. Conventionally driven investment in silicon photonics has now transformed our ability to engineer light in compact devices. Photons are naturally immune to many conventional forms of noise and most operations can already be completed with very high fidelity. There is a catch. Photons don’t interact directly, so conventional 2Q gates cannot easily be constructed.

Photonic startups have instead been driven to alternative, though formally equivalent, approaches to quantum computation. Photonics is also embedded as an enabling component in many other quantum technologies.

For a detailed recent review of integrated photonic quantum technologies see .

SWOT Analysis

Strengths

  • Light by its nature is immune to most conventional sources of noise; it requires no electric or magnetic shielding; no vacuum systems are needed and most operations can be undertaken at room temperature.
  • Integrated photonic technology has enjoyed large scale investment for conventional applications; the major part of which is compatible with current CMOS fabrication facilities .
  • Single photons can be manipulated with extremely high fidelity and at fast speed.
  • More general quantum states of light can also be directly manipulated; such ‘squeezed light’ provides an alternative quantum resource.
  • Silicon waveguides offer a small bend radius <1μm and thus very high component density, promising small footprint devices.

Opportunities

  • Multiple variations of the underlying technology have been developed, each with varying strengths and weaknesses. Silicon-on-insulator (SOI)  and silicon nitride (Si3N4) offer existing component ecosystems;  direct laser-writing on silica offers unique flexibility in fabrication; lndium phosphide  (InP), gallium arsenide(GaAs) and lithium niobate (LiNb03) offer unique opportunities for active components.
  • Strong synergies with techniques in quantum communications, including QKD hardware and quantum repeaters.
  • Strong synergies with advanced techniques in quantum sensing, where precision can often be improved by optical readout using squeezed light.
  • Natural fit for modular quantum networking and the Quantum Internet.

Weaknesses

  • Conventional 2Q gates are challenging. Photonic platforms are driven to alternative (though formally equivalent) approaches such as MBQC and CVQC.
  • Ideal photon sources are challenging. Processes such as SFWM and SPDC produce entangled photon pairs, but randomly and with low probability. Quantum dots promise generation on demand but require the integration of more exotic materials and the challenge of repeatably creating near-identical dots.
  • Sensitive detection technologies, such as SNSPD and PNR TES require cryogenic cooling to c.2K.
  • While cosmic photons have shown lifetimes of 13.7 billion years, coherent lifetime in optical fibre is more typically 150μs (this is mitigated by fast gate speed).

Threats

  • Though they share CMOS technology processes, wafers optimised for photonics differ from those optimised for electronic performance. To avoid performance compromises some type of flip-chip bonding is still likely to be necessary.
  • Initial development of know-how around circuit based QC could leave software developed for photonic architectures lagging.

Key Players and Approaches

Implementations of quantum computation with light are set to vary widely.

Measurement Based QC on SOI Platform

Rather than implementing the sequential 2Q gates of the circuit model, a large entangled ‘cluster state’ is initially formed. The computation is then performed using only measurement operations (this approach is therefore also sometimes known as one-way quantum computation). Fabricating photonic integrated circuits via a silicon-on-insulator process enables the complexity of circuit design required.

Notable Commercial Players:  PsiQ

  • SOI is the most developed silicon photonics platform; it leverages wide investment in SOI as part of mainstream CMOS technology.
  • Common circuit components are readily fabricated in silicon: waveguides, delay lines, beamsplitters, phase shifters, switches.
  • Silicon offers a small bend radius <1μm and thus very high component density.
  • PsiQ have presented striking progress with 3D error correction cells that seem to offer significantly improved error correction thresholds. These promise to be realisable via 3D cluster states.
  • SNSPD offer a high fidelity solution for photon detection; they do require cryogenic cooling, but only to 2K.
  • Low entangled source yields and photon loss on chip have so far proved limiting factors for integrated photonic devices.  The ‘stealth mode’ approach that PsiQ have adopted limits the opportunity for real progress to be evaluated.

PsiQ have not talked about NISQ applications, but instead are focussing on a ‘1 million’ qubit device in 5-8 years. This could be very disruptive. Other hardware approaches may struggle to replicate geometries available to photonics. Though formally equivalent, cluster state based computation also represents an alternative to the circuit based visualisation assumed by many early quantum software frameworks.

Continuous Variable QC on Silicon Nitride platform

Non-physicists introduced to the subject via the popular discussions of qubits might be forgiven for thinking that quantum mechanics is all about individual particles and discrete two-level systems. The truth is rather more subtle. More general quantum systems are described by continuous variables. One example is ‘squeezed’ states of light which can be used as an alternative basis for quantum computation. In this scheme qumodes takeover the role of qubits.

Notable Commercial Players: Xanadu

  • Silicon Nitride is an emerging branch of Silicon Photonics; it is supported by a growing industrial base (though not one as developed as for conventional SOI processes).
  • Sources of squeezed light, are simpler to work with than the heralded single-photons required for MBQC. Xanadu has developed technology for this that works on the Silicon Nitride platform .
  • Xanadu have a 12 qumode device in operation (though no published stats on this device are yet available).
  • This approach provides unique access to Gaussian Boson Sampling  (GBS) as a potential algorithm for NISQ applications.
  • Synergies to CV approaches in quantum communications.
  • Most of the device can run at room temperature, though photon state detection is done off-chip using cryogenically cooled detectors.
  • CVQC does provide a path to FTQC , however practical work is less developed than for other approaches .

Xanadu’s claims of applications for GBS in efficiently solving graph isomorphism related problems have been met with some scepticism in the expert community . However Fact Based Insight sees one of the other areas they identify as having great commercial promise. Analysing the vibrational modes of molecules opens up possibilities in novel approaches to catalyse chemical reactions. This has obvious commercial potential. This is a form of quantum simulation and the statistical basis of this problem has a ‘bosonic’ character. GBS seems to offer an inherently plausible approach. Previous demonstrations by Anthony Laing, the co-founder of Duality Quantum Photonics point to commercially relevant calculations being within reach . Duality itself is targeting creating photonic devices to address problems in this area within 3-5 years.

QuiX is a recent startup in the Netherlands developing a quantum processor on the Silicon Nitride platform.

Other Photonic

Orca Computing is a startup whose core technology is able to store and retrieve single photons and a QC architecture based on memories. In the near term this allows them to offer a unique ‘on-demand’ source of identical single photons for applications across the quantum sector. They also plan to target NISQ computing applications via an off-the-shelf version of MBQC using standard optical fibre rather than integrated components. Quantum memory promises to be a long term opportunity. Multiple overlapping revenue streams is a good strategy.

References

[1]
D. Gottesman, A. Kitaev, and J. Preskill, “Encoding a qubit in an oscillator,” Phys. Rev. A, vol. 64, no. 1, p. 012310, Jun. 2001, doi: 10.1103/PhysRevA.64.012310. Available: http://arxiv.org/abs/quant-ph/0008040. [Accessed: Jul. 06, 2020]
[1]
“Shtetl-Optimized » Blog Archive » Quantum computing motte-and-baileys.” Available: https://www.scottaaronson.com/blog/?p=4447. [Accessed: Jul. 18, 2020]
[1]
Y. Zhang et al., “Single-mode quadrature squeezing using dual-pump four-wave mixing in an integrated nanophotonic device,” arXiv:2001.09474 [physics, physics:quant-ph], Jan. 2020, Available: http://arxiv.org/abs/2001.09474. [Accessed: Jul. 04, 2020]
[1]
I. Tzitrin, J. E. Bourassa, N. C. Menicucci, and K. K. Sabapathy, “Progress towards practical qubit computation using approximate Gottesman-Kitaev-Preskill codes,” Phys. Rev. A, vol. 101, no. 3, p. 032315, Mar. 2020, doi: 10.1103/PhysRevA.101.032315. Available: http://arxiv.org/abs/1910.03673. [Accessed: Jul. 06, 2020]
[1]
J. Wang, F. Sciarrino, A. Laing, and M. G. Thompson, “Integrated Photonic Quantum Technologies,” Nat. Photonics, vol. 14, no. 5, pp. 273–284, 2020, doi: 10.1038/s41566-019-0532-1. Available: http://arxiv.org/abs/2005.01948. [Accessed: Jun. 21, 2020]
[1]
T. Rudolph, “Why I am optimistic about the silicon-photonic route to quantum computing,” arXiv:1607.08535 [quant-ph], Jul. 2016, Available: http://arxiv.org/abs/1607.08535. [Accessed: Jul. 04, 2020]
[1]
C. Sparrow et al., “Simulating the vibrational quantum dynamics of molecules using photonics,” Nature, vol. 557, no. 7707, pp. 660–667, May 2018, doi: 10.1038/s41586-018-0152-9. Available: https://www.nature.com/articles/s41586-018-0152-9. [Accessed: Jun. 21, 2020]
[1]
M. Szegedy, “What do QAOA energies reveal about graphs?,” arXiv:1912.12277 [quant-ph], Dec. 2019, Available: http://arxiv.org/abs/1912.12277. [Accessed: Apr. 22, 2020]

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David Shaw

About the Author

David Shaw has worked extensively in consulting, market analysis & advisory businesses across a wide range of sectors including Technology, Healthcare, Energy and Financial Services. He has held a number of senior executive roles in public and private companies. David studied Physics at Balliol College, Oxford and has a PhD in Particle Physics from UCL. He is a member of the Institute of Physics. Follow David on Twitter and LinkedIn