Collaborating Authors

Google Accelerates Quantum Computation with Classical Machine Learning


Tech giant Google's recent claim regarding quantum supremacy created a buzz in the computer science community and got global mainstream media talking about quantum computing breakthroughs. Yesterday Google fed the public's growing interest in the topic with a blog post introducing a study on improving quantum computation using classical machine learning. The qubit is the most basic constituent of quantum computing, and also poses one of the most significant challenges for the realization of near-term quantum computers. Various characteristics of qubits have made it challenging to control them. Google AI explains that issues such as imperfections in the control electronics can "impact the fidelity of the computation and thus limit the applications of near-term quantum devices."

Quantum Neural Network and Soft Quantum Computing Artificial Intelligence

A new paradigm of quantum computing, namely, soft quantum computing, is proposed for nonclassical computation using real world quantum systems with naturally occurring environment-induced decoherence and dissipation. As a specific example of soft quantum computing, we suggest a quantum neural network, where the neurons connect pairwise via the "controlled Kraus operations", hoping to pave an easier and more realistic way to quantum artificial intelligence and even to better understanding certain functioning of the human brain. Our quantum neuron model mimics as much as possible the realistic neurons and meanwhile, uses quantum laws for processing information. The quantum features of the noisy neural network are uncovered by the presence of quantum discord and by non-commutability of quantum operations. We believe that our model puts quantum computing into a wider context and inspires the hope to build a soft quantum computer much earlier than the standard one.

Quantum logic at a distance


Quantum computers could revolutionize how specific computational problems are solved that remain untractable even for the world's best supercomputers. However, although the basic elements of a quantum computer—realizing a register of qubits that preserve superposition states, controlling and reading out qubits individually, and performing quantum gates between them—have been scaled to a few dozen qubits, millions are needed to attack problems such as integer factorization. One approach for scaling up quantum computers is to “divide and conquer”—keep individual processing units smaller and connect many of them together. This approach leaves local processing nodes tractable but requires generation of entanglement and performance of quantum gates on qubits located at distant nodes to keep the advantages of quantum processing. On page 614 of this issue, Daiss et al. ([ 1 ][1]) made substantial progress toward this goal by performing quantum-logic operations on two distant qubits in an elementary network. Entangling distant qubits in quantum networks can enable distributed computing and secure data transmission . Basic quantum networks have been demonstrated with a few different systems such as ultracold atoms ([ 2 ][2]), trapped ions ([ 3 ][3]), color centers in diamond ([ 4 ][4]), and superconducting qubits ([ 5 ][5]). By using schemes to improve the quality of initially imperfect entanglement, e.g., by so-called entanglement distillation ([ 6 ][6]), it should be possible to build noise-resilient, error-corrected quantum networks that perform better than their individual components ([ 7 ][7]). The additional benefits of improved qubit addressability, reduced cross-talk, and improved connectivity between arbitrary qubits controlled through the network connections suggest that a distributed quantum computer could outperform one large computing core. Daiss et al. realized a quantum gate between two separated qubits in independent setups connected by a 60-m-long optical fiber. The qubits are implemented by internal spin states of two atoms that are trapped inside optical cavities. The qubits become connected when a single photon sent through the two setups is successively reflected from the two cavities and then detected ([ 8 ][8]). The presence of an atom strongly coupled to a cavity changes the reflection phase of the photon. A photon reflected off an empty resonant cavity undergoes a π phase shift. However, an atom strongly coupled to a cavity causes a frequency shift of the cavity resonance. This shift prevents photon entry upon reflection , and the reflected-photon phase remains unchanged. One qubit state of the atom, but not the other, couples strongly to the cavity, so one state does not change the photon reflection phase, and the other adds a π phase shift. An atom in a superposition state of its qubit levels will produce a corresponding superposition of the photon phase and create an atom-photon entangled state. When such an entangled photon is bounced off a second cavity and undergoes another atom-photon entangling operation with the second atom, a final state can be produced that corresponds to a NOT-gate operation. ![Figure][9] Large-scale quantum circuits Daiss et al. created quantum processors with single photons guided by optical fibers that were reflected successively by two atom–cavity devices. Scaling to multiple qubits could be achieved with large-scale photonic networks connecting optical cavities containing multiple atomic spin qubits. The full scheme is actually more complicated and requires encoding through polarization states, a measurement of the resulting combined atom-atom state based on the photon polarization, and a conditional change of the first atom's state that depends on the measurement result. Daiss et al. realized a controlled-NOT gate with <15% deviation from the ideal gate performance. Together with the simpler single-qubit gates, this result represents a complete toolbox to implement any kind of quantum logic operation. The scheme is heralded, meaning that it produces a measurable signal when the gate operation is successful and becomes immune against photon loss as an error source. At a first glance, the scheme requires surprisingly few resources—only a single photon that is reflected successively by two atomcavity devices. Indeed, strong atom-cavity coupling makes the scheme efficient as it increased the probability that the photon is reflected rather than lost through processes such as spontaneous emission. However, realizing such strongly coupled atom-cavity systems requires overcoming hurdles that include atom control, cavity performance, atomic coherence, and minimized photon loss. Also, the experiment was not yet operated with single photons but with attenuated laser pulses, which is easier to do but introduces errors caused by the presence of two-photon contributions and enforces low average photon numbers. Although all of these issues limit the efficiency and fidelity of the gate demonstrated by Daiss et al. , none of them introduce fundamental limits and could be improved in the future. Thus, it should be possible to scale up the system (see the figure). For example, multiple atoms coupled to a single cavity could allow the reflection of a single photon to immediately produce an N -qubit Toffoli gate ([ 8 ][8]), an element of the Grover's search algorithm. Also, elegant ways for scaling multiple cavities in interferometer-type configurations have been proposed ([ 9 ][10]) in which a single photon can generate an entangling quantum gate between any selected qubits in a network. Although formidable challenges remain, it is intriguing to imagine the possibilities when distributed quantum computers form a quantum internet ([ 10 ][11], [ 11 ][12]). 1. [↵][13]1. S. Daiss et al ., Science 371, 614 (2021). [OpenUrl][14][Abstract/FREE Full Text][15] 2. [↵][16]1. S. Ritter et al ., Nature Nature, 195 (2012). 3. [↵][17]1. D. L. Moehring et al ., Nature 449, 68 (2007). [OpenUrl][18][CrossRef][19][PubMed][20][Web of Science][21] 4. [↵][22]1. P. C. Humphreys et al ., Nature 558, 268 (2018). [OpenUrl][23] 5. [↵][24]1. N. Roch et al ., Phys. Rev. Lett. 112, 170501 (2014). [OpenUrl][25][CrossRef][26][PubMed][27] 6. [↵][28]1. N. Kalb et al ., Science 356, 928 (2017). [OpenUrl][29][Abstract/FREE Full Text][30] 7. [↵][31]1. N. H. Nickerson, 2. J. F. Fitzsimons, 3. S. C. Benjamin , Phys. Rev. X 4, 041041 (2014). [OpenUrl][32] 8. [↵][33]1. L.M. Duan, 2. B. Wang, 3. H. J. Kimble , Phys. Rev. A 72, 032333 (2005). [OpenUrl][34][CrossRef][35] 9. [↵][36]1. I. Cohen, 2. K. Molmer , Phys. Rev. A 98, 030302 (R) (2018). [OpenUrl][37][CrossRef][38] 10. [↵][39]1. H. J. Kimble , Nature 453, 1023 (2008). [OpenUrl][40][CrossRef][41][PubMed][42][Web of Science][43] 11. [↵][44]1. S. Wehner, 2. D. Elkouss, 3. R. Hanson , Science 362, eaam9288 (2018). 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Closing In on Quantum Error Correction

Communications of the ACM

After decades of research, quantum computers are approaching the scale at which they could outperform their "classical" counterparts on some problems. They will be truly practical, however, only when they implement quantum error correction, which combines many physical quantum bits, or qubits, into a logical qubit that preserves its quantum information even when its constituents are disrupted. Although this task once seemed impossible, theorists have developed multiple techniques for doing so, including "surface codes" that could be implemented in an integrated-circuit-like planar geometry. For ordinary binary data, errors can be corrected, for example, using the majority rule: A desired bit, whether 1 or 0, is first triplicated as 111 or 000. Later, even if one of the three bits has been corrupted, the other two "outvote" it and allow recovery of the original data.

Roadmap for 1000 Qubits Fault-tolerant Quantum Computers - Amit Ray


How many qubits are needed to out-perform conventional computers, how to protect a quantum computer from the effects of decoherence and how to design more than 1000 qubits fault-tolerant large scale quantum computers, these are the three basic questions we want to deal in this article. Qubit technologies, qubit quality, qubit count, qubit connectivity and qubit architectures are the five key areas of quantum computing are discussed. Earlier we have discussed 7 Core Qubit Technologies for Quantum Computing, 7 Key Requirements for Quantum Computing. Spin-orbit Coupling Qubits for Quantum Computing and AI, Quantum Computing Algorithms for Artificial Intelligence, Quantum Computing and Artificial Intelligence, Quantum Computing with Many World Interpretation Scopes and Challenges and Quantum Computer with Superconductivity at Room Temperature. Here, we will focus on practical issues related to designing large-scale quantum computers.