Upper bounds on quantum query complexity inspired by the Elitzur-Vaidman bomb tester

Inspired by the Elitzur-Vaidman bomb testing problem [arXiv:hep-th/9305002], we introduce a new query complexity model, which we call bomb query complexity $B(f)$. We investigate its relationship with the usual quantum query complexity $Q(f)$, and show that $B(f)=\Theta(Q(f)^2)$. This result gives a new method to upper bound the quantum query complexity: we give a method of finding bomb query algorithms from classical algorithms, which then provide nonconstructive upper bounds on $Q(f)=\Theta(\sqrt{B(f)})$. We subsequently were able to give explicit quantum algorithms matching our upper bound method. We apply this method on the single-source shortest paths problem on unweighted graphs, obtaining an algorithm with $O(n^{1.5})$ quantum query complexity, improving the best known algorithm of $O(n^{1.5}\sqrt{\log n})$ [arXiv:quant-ph/0606127]. Applying this method to the maximum bipartite matching problem gives an $O(n^{1.75})$ algorithm, improving the best known trivial $O(n^2)$ upper bound.Comment: 32 pages. Minor revisions and corrections. Regev and Schiff's proof that P(OR) = \Omega(N) remove

Design, synthesis and testing of novel anti-cancer agents targeting secretory pathway calcium ATPase

Secretory pathway calcium ATPase (SPCA) 1 was found to be associated with basal-like breast cancers, which had the poorest prognosis with minimal therapeutic agents available. Increased expression of insulin-like growth factor receptor was identified in a study to possess a strong involvement in cancer initiation, proliferation and resistance to anti-cancer therapy. This finding had provided a window of opportunity to place SPCA1 as a new therapeutic target for basal-like breast cancer. The main aims were to fully understand the role of SPCA1 in basal-like breast cancer and to design, synthesise and test chemical compounds that would inhibit the growth of basal-like breast cancer cells in vitro. Additional focus was also placed on discovering sarcoplasmic-endoplasmic reticulum calcium ATPase (SERCA) inhibitors to streamline the drug discovery process. The use of molecular modelling, virtual screening, chemical synthesis and biological assays had assisted with the decision of selecting potential compounds. At least three out of seven tested compounds had an effect on the intracellular calcium signals. However, the potency and selectivity of these compounds would need to be improved to become better SERCA inhibitors. Therefore more future work is warranted to further refine the potency and selectivity of these compounds on the target receptors

The use of computer-aided drug design in small molecule drug discovery

Drug discovery is one of the most challenging research fields that contributes to the birth of novel drugs for therapeutic use. Due to the complexity and intricate nature of the research, lengthy processes are involved in identifying potential hit molecules for a therapeutic target. To shorten the time required to reach the hit-to-lead stage, computer-aided drug design (CADD) has been used to expedite the process and reduce laboratory expenses. Common strategies used within CADD involve structure-based drug design (SBDD) and ligand-based drug design (LBDD). Both strategies were used extensively in two projects showing the complementarity of each strategy throughout the process. In this work, two separate drug discovery projects are detailed: Design, synthesis and molecular docking study of novel tetrahydrocurcumin analogues as potential sarcoplasmic-endoplasmic reticulum calcium ATPases (SERCA) inhibitors – details the identification, synthesis and testing of potential hit candidate(s) targeting SERCA by using SBDD Filamenting temperature-sensitive mutant Z (FtsZ) as therapeutic target in ligand-based drug design – details the identification, synthesis and testing of potential hit molecule(s) targeting FtsZ In the first project, homology modelling and virtual compound library screening were utilised as the SBDD methods to identify potential hit molecules for testing in P-type calcium ATPases such as SERCA. Preliminary results have found compound 20, an analogue of tetrahydrocurcumin, to show some SERCA inhibitory effect at 300µM based on a SERCA-specific calcium signalling assay performed via fluorometric imaging plate reader. Molecular docking study has also reflected this outcome with desirable ligand-protein binding energies found for 20 when compared with other tested ligands. Pharmacophore screening was used as the main LBDD method in the second project to identify probable hit candidates targeting FtsZ. Potential ligands were synthesised, and tested for antibacterial effect in Bacillus Subtilis strain 168 (Bs168) and Streptococcus pneumoniae strain R6 (SpnR6) cells. One of the tetrahydrocurcumin analogues, compound 4, was found to have minimum inhibitory concentration (MIC) ≤ 10 µM in Bs168 cells and ≤ 2 µM in spnR6 cells. The IC50 values for 4 were 9.1 ± 0.01 µM and 1 ± 0.01 µM in Bs168 and SpnR6 cells respectively. The MIC of 4 was found to be very similar to the MIC of compound 1, a known hit compound targeting against Bs168 cells. On the other hand, the MIC of 4 was lower than the MIC (> 64 µg/mL) of a well-known FtsZ inhibitor, PC190723, against S. pneumoniae. Subsequent molecular docking analyses were completed to evaluate the ligand-protein binding energies to correlate against the testing results. Both compounds 20 and 4 possess some structural similarities and differences that may confer their different effects in these protein targets, which render both with potentials to become the next lead molecules for future development

Semi-Supervised Domain Adaptation with Source Label Adaptation

Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen target data with a few labeled and lots of unlabeled target data, along with many labeled source data from a related domain. Current SSDA approaches usually aim at aligning the target data to the labeled source data with feature space mapping and pseudo-label assignments. Nevertheless, such a source-oriented model can sometimes align the target data to source data of the wrong classes, degrading the classification performance. This paper presents a novel source-adaptive paradigm that adapts the source data to match the target data. Our key idea is to view the source data as a noisily-labeled version of the ideal target data. Then, we propose an SSDA model that cleans up the label noise dynamically with the help of a robust cleaner component designed from the target perspective. Since the paradigm is very different from the core ideas behind existing SSDA approaches, our proposed model can be easily coupled with them to improve their performance. Empirical results on two state-of-the-art SSDA approaches demonstrate that the proposed model effectively cleans up the noise within the source labels and exhibits superior performance over those approaches across benchmark datasets. Our code is available at https://github.com/chu0802/SLA .Comment: Accepted by CVPR 202