Kuantum hesaplamanın makine öğrenimi ve yapay zeka üzerinde gerçekleştirebileceği değişimin kapsamı tartışmaya açık olsa da, şimdiler de birçok uzman, kuantum bilişimin makine öğrenimi ve yapay zekayı bir düzeyde kesinlikle değiştireceğinden neredeyse hep fikir. Örneğin; BBVA’dan analistler, kuantum bilişim ve yapay zeka uygulamaları arasındaki doğal sinerjinin, kuantum makine öğreniminin nihayetinde klasik makine öğrenmesinden daha iyi olabileceğini bir neden olarak işaret ediyorlar.
BBVA’nın Yeni Dijital İşletmeler alanıyla işbirliği yapan bir kuantum algoritması araştırmacısı Samuel Fernández Lorenzo bu konuda şöyle bir yorum getiriyor:
En azından geleneksel bilgisayarların kullandığı bazı makine öğrenmesi modelleri için kuantum makine öğrenimi klasik makine öğreniminden daha verimli olabilir. Hala bu modellerin pratik uygulamalarda ne durumda olduğunu keşfetmeliyiz.”
Peki ya bu değişim nasıl olabilir?
Dilerseniz, hep birlikte kuantum bilişimin makine öğrenimini ve yapay zekayı sonsuza kadar değiştirebileceği yollara bir göz atalım.
1-) Büyük Veri Miktarlarını İşleme
Günümüz modern dünyasında, makine öğrenmesi ve yapay zeka verileri oldukça fazla veri içermektedir. Kuantum bilgisayarlar büyük miktarda veriyi yönetebilmek için ideal çözümler sunmaktadır. Kuantum bilgisayar tasarımının her bir yinelemesi ve kuantum hata düzeltme kodundaki iyileştirmelerle, programcılar daha fazla veriyi yönetmek için kübitlerin (kuantum bitleri) potansiyelini daha iyi yönetebilirler.
Klasik hesaplamada, programlama sırasında kullanılan bilgisayar dili mantıksal operatörleri sayesinde problemleri nasıl işleyeceğimizi biliyoruz. Bundan faydalanarak klasik bit hesaplamada mümkün olmayan işlemler bir kuantum bilgisayar ile yapılabilir. Örnek olarak: bir kuantum bilgisayar üzerinde N-kübitle oluşturulabilecek tüm sayılar ve olasılıklar (3 kübit varsa, 8 eş zamanlı olası permütasyon olacaktır.) üst üste bindirilir. 1.000 kübit ile üstel olasılıklar klasik hesaplamadaki üstünlükleri aşacaktır.
2-) Daha İyi Modeller İnşa Etmek
İlaç sektörü, tıp, yaşam bilimleri ve finans gibi çeşitli endüstriler neredeyse klasik bilgi işlem kapasitelerinin neredeyse sonundalar. Bu endüstriler, klasik bilgisayarların üretemediği karmaşık modellere ihtiyaç duymaktadır. Kuantum bilgisayarlar ise en karmaşık durumları modelleme potansiyeline sahiptir. Kuantum teknolojileri ile daha iyi modeller oluşturulabilirse, yeni ilaç keşiflerine ve öngörülere, hastalıklar için daha iyi tedavilere, finansal patlama risklerinin azalmasına ve gelişmiş lojistiklere olanak sağlayabilirler.
3-) Daha Doğru Algoritmalar
Kuantum makine öğrenmesi (Quantum machine learning) araştırmacıları daha iyi kuantum bilgisayar algoritmaları geliştirmenin yollarını bulmaya çalışıyorlar.
“Bu alanda, daha önce ortaya konmuş olan farklı kuantum makine öğrenimi algoritmaları çalışmalarına dayanarak, hızlı ve daha kesinlikle çalışan algoritmaları görebiliriz.” diye açıklıyor Fernández Lorenzo.
Araştırmacılara göre, kuantum hesaplamanın gözetimsiz öğrenme ve gözetimli öğrenme gibi geleneksel modeller ve algoritmaları üzerinde etkisi yaratması beklenmektedir. Kuantum bilişimin özel yeteneği, belirli küresel özellikleri belirli bir ayrıntıda değil, bir veri kümesinde saptamaya yardımcı olabilir.
4-) Çoklu Veri Kümesi Kullanma
Fütürist ve stratejik danışman Bernard Marr’a göre, sorun genellikle yeterli veri olmadığı veya çok fazla veri olmadığı değil, sorunun çeşitli veri kümelerine yerleştirilmek zorunda olmasıdır. Kuantum bilgisayarların çok daha hızlı ve daha kolay analiz için farklı veri kümelerinin entegrasyonunu ele alabilmesi büyük bir fark yaratabilir.
Kuantum bilgisayarlar, makine öğrenimimizi ve yapay zeka yeteneklerimizi geliştirecek ve dönüştürecek muazzam veri setlerimizin hızlı analizine ve entegrasyonuna izin verebilir.
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