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Abstract We introduce a family of variational quantum algorithms, which we coin as quantum iterative power algorithms (QIPAs), and demonstrate their capabilities as applied to global-optimization numerical experiments. Specifically, we demonstrate the QIPA based on a double exponential oracle as applied to ground state optimization of theH₂molecule, search for the transmon qubit ground-state, and biprime factorization. Our results indicate that QIPA outperforms quantum imaginary time evolution (QITE) and requires a polynomial number of queries to reach convergence even with exponentially small overlap between an initial quantum state and the final desired quantum state, under some circumstances. We analytically show that there exists an exponential amplitude amplification at every step of the variational quantum algorithm, provided the initial wavefunction has non-vanishing probability with the desired state and that the unique maximum of the oracle is given by ${\lambda }_1>0$, while all other values are given by the same value $0<{\lambda }_2<{\lambda }_1$(hereλcan be taken as eigenvalues of the problem Hamiltonian). The generality of the global-optimization method presented here invites further application to other problems that currently have not been explored with QITE-based near-term quantum computing algorithms. Such approaches could facilitate identification of reaction pathways and transition states in chemical physics, as well as optimization in a broad range of machine learning applications. The method also provides a general framework for adaptation of a class of classical optimization algorithms to quantum computers to further broaden the range of algorithms amenable to implementation on current noisy intermediate-scale quantum computers. 

Low-cost self-driving labs (SDLs) offer faster prototyping, low-risk hands-on experience, and a test bed for sophisticated experimental planning software which helps us develop state-of-the-art SDLs. 

The design of organic light emitting diode (OLED) materials with the potential for exhibiting thermally-activated delayed fluorescence (TADF) is reported. Using computational methods (DFT/TD-DFT) as a guiding tool, six materials with a benzobisoxazole (BBO) core and donor–acceptor–donor architectures were designed by changing the conjugation position of carbazole-substituted phenyl substituents and the type of BBO isomer ( cis - vs. trans -). Experimental steady-state and transient absorption spectroscopic techniques were utilized to probe the TADF activity of these molecules. Each material was then used in host–guest OLED devices as either near-UV dopants or host with low singlet-triplet energy differences. The electroluminescent properties show that when used as dopants these materials provide near-UV emission (CIE y < 0.06 and CIE x = 0.16), whereas when used as hosts, these materials show reduced operating voltages and increased performance efficiencies when compared to commercial materials. 

Iterating machine learning with robotic experimentation uncovered higher-yielding conditions for a common coupling reaction. 

Abstract Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield. 

To accelerate materials discovery, computational methods such as inverse materials design have been proposed to predict the properties of target compounds of interest for specific applications. This in silico process can be used to guide subsequent synthesis and characterization. Inverse design is especially relevant for the field of organic molecules, for which there are nearly infinite synthetic modifications possible. With a target application of UV-absorbing, visibly transparent solar cells in mind, we calculated the orbital and transition energies of over 360 possible coronene derivatives. Our screening, or the constraints we imposed on the calculated series, resulted in the selection of three new derivatives, namely contorted pentabenzocoronene (cPBC), contorted tetrabenzocoronene (cTBC), and contorted tetrabenzofuranylbenzocoronene (cTBFBC) for synthesis and characterization. Our materials characterization found agreement between our calculated and experimental energy values, and through testing of these materials in organic photovoltaic (OPV) devices, we fabricated solar cells with an open-circuit voltage of 1.84 V and an average visible transparency of 88% of the active layer; both quantities exceed previous records for visibly transparent coronene-based solar cells. This work highlights the promise of inverse materials design for future materials discovery, as well as improvements to an exciting application of UV-targeted solar cells.