Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability

An active area of investigation in the search for quantum advantage is quantum machine learning. Quantum machine learning, and parameterized quantum circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space. But is the ability of a quantum circuit to uniformly address the Hilbert space a good indicator of classification accuracy? In our work, we use methods and quantifications from prior art to perform a numerical study in order to evaluate the level of correlation. We find a moderate to strong correlation between the ability of the circuit to uniformly address the Hilbert space and the achieved classification accuracy for circuits that entail a single embedding layer followed by 1 or 2 circuit designs. This is based on our study encompassing 19 circuits in both 1- and 2-layer configurations, evaluated on 9 datasets of increasing difficulty. We also evaluate the correlation between entangling capability and classification accuracy in a similar setup, and find a weak correlation. Future work will focus on evaluating if this holds for different circuit designs

Training quantum embedding kernels on near-term quantum computers

Kernel methods are a cornerstone of classical machine learning. The idea of using quantum computers to compute kernels has recently attracted attention. Quantum embedding kernels (QEKs), constructed by embedding data into the Hilbert space of a quantum computer, are a particular quantum kernel technique that is particularly suitable for noisy intermediate-scale quantum devices. Unfortunately, kernel methods face three major problems: Constructing the kernel matrix has quadratic computational complexity in the number of training samples, choosing the right kernel function is nontrivial, and the effects of noise are unknown. In this work, we addressed the latter two. In particular, we introduced the notion of trainable QEKs, based on the idea of classical model optimization methods. To train the parameters of the QEK, we proposed the use of kernel-target alignment. We verified the feasibility of this method, and showed that for our experimental setup we could reduce the training error significantly. Furthermore, we investigated the effects of device and finite sampling noise, and we evaluated various mitigation techniques numerically on classical hardware. We took the best performing strategy and evaluated it on data from a real quantum processing unit. We found that using this mitigation strategy demonstrated an increased kernel matrix quality

Single-component gradient rules for variational quantum algorithms

Many near-term quantum computing algorithms are conceived as variational quantum algorithms, in which parameterized quantum circuits are optimized in a hybrid quantum-classical setup. Examples are variational quantum eigensolvers, quantum approximate optimization algorithms as well as various algorithms in the context of quantum-assisted machine learning. A common bottleneck of any such algorithm is constituted by the optimization of the variational parameters. A popular set of optimization methods work on the estimate of the gradient, obtained by means of circuit evaluations. We will refer to the way in which one can combine these circuit evaluations as gradient rules. This work provides a comprehensive picture of the family of gradient rules that vary parameters of quantum gates individually. The most prominent known members of this family are the parameter shift rule and the finite differences method. To unite this family, we propose a generalized parameter shift rule that expresses all members of the aforementioned family as special cases, and discuss how all of these can be seen as providing access to a linear combination of exact first- and second-order derivatives. We further prove that a parameter shift rule with one non-shifted evaluation and only one shifted circuit evaluation can not exist does not exist, and introduce a novel perspective for approaching new gradient rules.Comment: 9 pages, 0 figure

SCI-FII: Speculative Conversational Interface Framework for Incremental Inference on Modularized Services

We propose Sci-Fii, a speculative conversational interface framework for incremental inference on modularized services. To build one's own conversational interface with existing business logic, cloud-based modularized services offer a suite of ready-to-use components to ease development, ensure cross-platform flexibility, and encapsulate computational complexities. However developing with the modularized services often results in a chain of discrete modules with limited inter-module data sharing, which yields unnecessarily long response times of the conversational interface, aggravates user experiences, and eventually harms user retention. Sci-Fii offers a uniform framework that enables existing serviced modules to benefit from intermediate data and early parallel execution. Transparent to developers, Sci-Fii helps the end-to-end conversational interface system work fluidly and exhibit faster and more natural response times.N

SCI-FII: Speculative Conversational Interface Framework for Incremental Inference on Modularized Services

We propose Sci-Fii, a speculative conversational interface framework for incremental inference on modularized services. To build one's own conversational interface with existing business logic, cloud-based modularized services offer a suite of ready-to-use components to ease development, ensure cross-platform flexibility, and encapsulate computational complexities. However developing with the modularized services often results in a chain of discrete modules with limited inter-module data sharing, which yields unnecessarily long response times of the conversational interface, aggravates user experiences, and eventually harms user retention. Sci-Fii offers a uniform framework that enables existing serviced modules to benefit from intermediate data and early parallel execution. Transparent to developers, Sci-Fii helps the end-to-end conversational interface system work fluidly and exhibit faster and more natural response times.1

Accelerating Conversational Agents Built with Off-the-shelf Modularized Services

Today’s common practice in developing conversational agents is pipelining off-the-shelf modularized services as ready-made building blocks. However, the discrete and sequential nature of the modules yields long response latency. We introduce Sci-Fii, a speculative inference framework accelerating conversational agent systems built with off-the-shelf modules, while keeping the modules unchanged.11Nsciescopu

Accelerating Conversational Agents Built With Off-the-Shelf Modularized Services

Today's common practice in developing conversational agents is pipelining off-the-shelf modularized services as ready-made building blocks. However, the discrete and sequential nature of the modules yields long response latency. We introduce Sci-Fii, a speculative inference framework accelerating conversational agent systems built with off-the-shelf modules, while keeping the modules unchanged.N

Training quantum embedding kernels on near-term quantum computers

Kernel methods are a cornerstone of classical machine learning. The idea of using quantum computers to compute kernels has recently attracted attention. Quantum embedding kernels (QEKs), constructed by embedding data into the Hilbert space of a quantum computer, are a particular quantum kernel technique that is particularly suitable for noisy intermediate-scale quantum devices. Unfortunately, kernel methods face three major problems: Constructing the kernel matrix has quadratic computational complexity in the number of training samples, choosing the right kernel function is nontrivial, and the effects of noise are unknown. In this work, we addressed the latter two. In particular, we introduced the notion of trainable QEKs, based on the idea of classical model optimization methods. To train the parameters of the QEK, we proposed the use of kernel-target alignment. We verified the feasibility of this method, and showed that for our experimental setup we could reduce the training error significantly. Furthermore, we investigated the effects of device and finite sampling noise, and we evaluated various mitigation techniques numerically on classical hardware. We took the best performing strategy and evaluated it on data from a real quantum processing unit. We found that using this mitigation strategy demonstrated an increased kernel matrix quality