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Hitachi Cambridge Seminars

Here is a list of the upcoming 'Hitachi Cambridge Seminars' :

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Some of the previous talks can be found below :

Hamiltonian quantum computing

This talk shows how to recast quantum algorithms in a purely Hamiltonian form, substituting time-dependent Hamiltonian controls for the conventional quantum logic gate picture. Using the example of the quantum singular value transformation (“the mother of all quantum algorithms,” according to Ike Chuang), I show that Hamiltonian quantum computing can supply significant advantages in terms of time and decoherence compared with the gate based model.

[1] ‘Hamiltonian singular value transformation and inverse block encoding’, Seth Lloyd, Bobak T. Kiani, David R. M. Arvidsson-Shukur, Samuel Bosch, Giacomo De Palma, William M. Kaminsky, Zi-Wen Liu, Milad Marvian, arXiv Preprint:

Where: Virtually on Zoom

Training deep quantum neural networks

Machine learning, particularly as applied to deep neural networks via the back-propagation algorithm, has brought enormous technological and societal change. With the advent of quantum technology it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. In my talk I will present a truly quantum analogue of classical neurons and explain how to use it to form a quantum feed-forward neural networks capable of universal quantum computation. For training these networks we use the fidelity as a cost function and benchmark the proposal for the quantum task of learning an unknown unitary operation. We find remarkable generalization behavior and robustness to noisy training data. My talk will be based on a recent work of us [1]. For digging deeper in to the topic after the talk I would recommend reading about finding an optimal lower bound on the probability that such a trained network gives an incorrect output for a random input [2] and about considering graph-structured quantum data for training our quantum neural networks [3].


Where: Virtually on Zoom

Silicon MOS quantum dots for spin-based quantum computation

Quantum computers are expected to outperform conventional computers for a range of important problems, from molecular simulation to search algorithms, once they can be scaled up to large numbers of quantum bits (qubits), typically millions. Spin qubits in silicon MOS quantum dots are one of the big contenders for a scalable, solid state-based quantum computing platform. Here, the qubits are encoded as the spin states of individual electrons confined in electrostatically-gated quantum dots. The great potential of this system has been demonstrated through various experiments over the last few years, with coherence times of up to T2=28 ms, single qubit control fidelities of 99.96%, and two-qubit control fidelities of 98%.

In my presentation, I will give an introduction to the SiMOS quantum dot spin qubits that we employ at UNSW Sydney. I will showcase some of the key experiments of the last years related to experimental challenges of scaling a silicon-CMOS based quantum processor up to the millions of qubits that will be required for fault-tolerant quantum computing. In particular, I will present our results of operating silicon spin qubits at temperatures above 1 K [1,2] that are important for the integration of conventional CMOS control electronics with the qubit system, and global control techniques that allow for the control of many qubits simultaneously.

[1] C. H. Yang, R. C. C. Leon, J. C. C. Hwang, A. Saraiva, T. Tanttu, W. Huang, J. Camirand Lemyre, K. W. Chan, K. Y. Tan, F. E. Hudson, K. M. Itoh, A. Morello, M. Pioro-Ladrière, A. Laucht, and A. S. Dzurak. Operation of a silicon quantum processor unit cell above one kelvin. Nature 580, 350 (2020).

[2] J. Y. Huang, W. H. Lim, R. C. C. Leon, C. H. Yang, F. E. Hudson, C. C. Escott, A. Saraiva, A. S. Dzurak, and A. Laucht. A High-Sensitivity Charge Sensor for Silicon Qubits above 1 K. Nano Letters 21, 6328 (2021).

[3] E. Vahapoglu, J. P. Slack-Smith, R. C. C. Leon, W. H. Lim, F. E. Hudson, T. Day, J. D. Cifuentes, T. Tanttu, C. H. Yang, A. Saraiva, M. L. W. Thewalt, A. Laucht, A. S. Dzurak, and J. J. Pla. Coherent control of electron spin qubits in silicon using a global field. arXiv:2107.14622 (2021).

Where: Virtually on Zoom

Measurement, information, control, decoherence

In this talk I will discuss some basic ideas in quantum measurement from the perspective of experiments that are readily available today in the circuit quantum electrodynamics architecture. We will focus on a recent experiment where we test an entropic uncertainty relation involving weak and projective measurements. Our story spans some of the oldest ideas in quantum mechanics (uncertainty relations), involves some of the most controversial (weak values), pulls on ideas in information theory (entropies), and will be presented from the humble perspective of an experimentalist trying to make his way through the fascinating world of quantum physics.


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Where: Virtually on Zoom

Negative quasiprobabilities enhance phase estimation in quantum-optics experiment

Hallmarks of quantum theory, such as operators’ failure to commute, impose fundamental limits on measurement precision. Foundational studies of these limits have pushed measurements and metrology to the bleeding edge. Inspired by a recent foundational result connecting metrology with quasiprobabilities [1], quantum generalizations of probabilities, we discover a filtering technique that promises an—in principle—unlimited advantage in the information rate of trials that survive the filter. We implement this filter in a proof-of-principle optical measurement of a waveplate’s birefringent phase and amplify the information per detected photon by over two orders of magnitude. We find the theoretically unlimited advantage to be bounded in practice because the filter also amplifies systematic errors. We crystallize the relationship between enhanced precision and negative quasiprobabilities by deriving an equality for pure states, confirmed by our data, between the postselected information-rate and a function of a quasiprobability distribution.

[1] D. R. M. Arvidsson-Shukur, N. Yunger Halpern, H. V. Lepage, A. A. Lasek, C. H. W. Barnes, and S. Lloyd, Quantumadvantage in postselected metrology, Nature Communications11, 3775 (2020)

Where: Virtually on Zoom

Quantum supremacy and quantum machine learning with analog quantum simulators

I will start with a general introduction on quantum simulation and computation with near term processors, the state of the art and the current race for building operational quantum processors. I will then review our work with Google on quantum simulations with superconducting qubits [1] and as well as our recent works in analog quantum supremacy [2] and quantum machine learning [3] with NISQ devices. If time, I will briefly summarise our work on qubit efficient quadratic optimization algorithms [4]. The first part of the talk should be accessible to non-specialists.P. Roushan, C. Neill, ...D.G. Angelakis, and J. Martinis. Science, 01 Dec 2017: Vol. 358, Issue 6367, (2017)

signatures of many-body localization with interacting photons

[2] Quantum supremacy and quantum phase transitionsJ. Tangpanitanon, S. Thanasilp, M. A. Lemonde, N. Dangiam, D. G. AngelakisPhys. Rev. B 103 , 165132 (2021)

[3] Expressibility and trainability of parameterized analog quantum systems for machine learning applicationsJ. Tangpanitanon, S. Thanasilp, M. A. Lemonde, N. Dangiam, D. G. AngelakisPhys. Rev. Research 2, 043364 2020

[4] Qubit efficient algorithms for binary optimization problemsB. Tan, M. A. Lemonde, S. Thanasilp, J. Tangpanitanon, D. G. AngelakisQuantum 5, 454 (2021) ]

Where: Virtually on Zoom

Organic neuromorphic electronics and biohybrid systems

Neuromorphic computing could address the inherent limitations of conventional silicon technology in dedicated machine learning applications. Recent work on large crossbar arrays of two-terminal memristive devices has led to the development of promising neuromorphic systems. However, delivering a compact and efficient parallel computing technology that is capable of embedding artificial neural networks in hardware remains a significant challenge.

Organic electronic materials have shown potential to overcome some of these limitations. This talk describes state-of-the-art organic neuromorphic devices and provides an overview of the current challenges in the field and attempts to address them. I demonstrate a novel concept based on an organic electrochemical transistor and show how we can use these devices in trainable biosensors and smart autonomous robotics.

Next to that, organic electronic materials have the potential to operate at the interface with biology. This can pave the way for novel architectures with bio-inspired features, offering promising solutions for the manipulation and the processing of biological signals and potential applications ranging from brain-computer-interfaces and smart robotics to bioinformatics. I will highlight our recent efforts for such hybrid biological memory devices.

[1] van de Burgt, Nature Materials, 2017, doi:10.1038/nmat4856[2] van de Burgt, Nature Electronics, 2018, doi:10.1038/s41928-018-0103-3[3] Keene, Nature Materials, 2020, doi:10.1038/s41563-020-0703-y

Where: Virtually on Zoom

Quantum Error Suppression

The theory of quantum fault-tolerance ensures that quantum computers can operate reliably in the presence of decoherence and noise. Although in principle the existence of this threshold means that scalable quantum computation is possible, in practice the value of this threshold and the required overhead are very important, as achieving them in experiments remain extremely challenging. One approach to reducing these requirements is to use active error correction in combination with passive methods that provide additional robustness against instability or noise. In this talk, we will explore various passive error reduction methods in different experimental setups, and will discuss how they can be thought as manifestations of “quantum Zeno effect”.

Where: Virtually on Zoom

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